{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "522f8aaa-a245-4295-a23a-6183f489b0cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from samplics.utils.types import PopParam\n",
    "from samplics.categorical import Tabulation, CrossTabulation\n",
    "import statsmodels.formula.api as smf\n",
    "from stargazer.stargazer import Stargazer\n",
    "from IPython.display import HTML\n",
    "import warnings\n",
    "import os\n",
    "thepath=os.getcwd()+r\"\\ivs_allwaves_fordataverse.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd93f8d7-587a-46f3-9d6f-f96e41b0b52e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ivs_table(avar,groupers,df,useeqwght=False,excludena=True,namegroups=True):\n",
    "    use=df.copy()\n",
    "    if excludena is True:\n",
    "        use=use.loc[pd.notna(use[avar])]    \n",
    "    if groupers is None:\n",
    "        groupers=['group']\n",
    "        use['group']='All'\n",
    "                \n",
    "    if 'Country' in groupers:\n",
    "        use['new_wght']=use['S017']\n",
    "    elif useeqwght is True:\n",
    "        use['new_wght']=use['S017']*use['S018']\n",
    "    else:\n",
    "        use['new_wght']=use['S017']*use['pwght']\n",
    "\n",
    "    use['groupsum']=use.groupby(groupers,observed=True)['new_wght'].transform('sum')\n",
    "    use['pc']=use['new_wght']/use['groupsum']*100\n",
    "    use['group_count']=use.groupby(groupers,observed=True)[avar].transform('count')\n",
    "    partway=use.groupby(groupers+[avar],observed=True).agg({'pc' : 'sum', 'group_count':'mean'}).reset_index()\n",
    "    partway=partway.loc[(pd.notna(partway['group_count']))].copy()\n",
    "    toreturn=partway.copy()  #bug - https://stackoverflow.com/questions/59628014/pandas-pivot-table-is-giving-the-error-valueerror-the-name-none-occurs-multiple\n",
    "    l2=groupers+['group_count']\n",
    "    toreturn=toreturn.pivot(index=l2, columns=avar, values=['pc']).reset_index()\n",
    "    toreturn.columns = ['_'.join(str(x) for x in col_name).rstrip('_') if len(col_name)==2 else col_name for col_name in toreturn.columns.to_flat_index()]\n",
    "    return(toreturn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84cc134d-eae1-4b9c-909c-0534b4021581",
   "metadata": {},
   "outputs": [],
   "source": [
    "def makescattertable(avar):\n",
    "    X=ivs_table('jobs',['Country','Year','S021']+[avar],df=ivs)\n",
    "    toreturn=X.pivot(index=['Country','Year','S021'],columns=avar,values=['group_count','pc_Agree','pc_Neutral','pc_Disagree']).reset_index()\n",
    "    toreturn.columns = ['_'.join(str(x) for x in col_name).rstrip('_') if len(col_name)==2 else col_name for col_name in toreturn.columns.to_flat_index()]\n",
    "    use=ivs.loc[pd.notna(ivs['jobs'])].copy().groupby(['Country','Year','S021'])\n",
    "    ctab=CrossTabulation(param=PopParam.prop)\n",
    "    toappend=[]\n",
    "    for adf in [use.get_group(x) for x in use.groups]:\n",
    "        adf=adf.loc[((pd.notna(adf[avar]))&(pd.notna(adf['jobs'])))].copy()\n",
    "        if len(adf)>0:\n",
    "            try:\n",
    "                ctab.tabulate(\n",
    "                vars=adf[[avar, 'jobs']],\n",
    "                samp_weight=adf['S017'],\n",
    "                remove_nan=True,\n",
    "                )\n",
    "                results=pd.DataFrame({'Country':adf.Country.unique().tolist()[0],\n",
    "                                      'Year':adf.Year.unique().tolist()[0],\n",
    "                                      'S021':adf.S021.unique().tolist()[0],\n",
    "                                               'chisq':ctab.stats.get('Pearson-Unadj').get('chisq_value'),\n",
    "                                               'pvalue':ctab.stats.get('Pearson-Unadj').get('p_value')\n",
    "                                               },index=[0]\n",
    "                                    )\n",
    "                toappend=toappend+[results]\n",
    "            except:\n",
    "                pass\n",
    "    toreturn=toreturn.merge(pd.concat(toappend,ignore_index=True),how='left')\n",
    "    return(toreturn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f05a22b-0ef8-41c0-ad84-9564951bfbd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeappendtable(avar,dv='jobs',returnall=False):\n",
    "    j=ivs_table(dv,[avar],ivs)\n",
    "    print(\"FULL SAMPLE RESULTS\")\n",
    "    print(j)\n",
    "    ctab=CrossTabulation(param=PopParam.prop)\n",
    "    ctab.tabulate(\n",
    "        vars=ivs[[avar, dv]],\n",
    "        samp_weight=ivs['S017'],\n",
    "        remove_nan=True,\n",
    "    )    \n",
    "    print(ctab)\n",
    "    # bysample=makescattertable(avar)\n",
    "    # if returnall is True:\n",
    "    #     return(j,bysample,ctab.stats)\n",
    "    # else:\n",
    "    # return(bysample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "393360ea-0c0a-42cd-ae8f-839e59d8d87e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'iso3n', 'mostrecent', 'Region', 'subregion',\n",
       "       'cincome', 'cimmig', 'regime', 'pwght', 'A124_06', 'C002', 'E033',\n",
       "       'G001', 'G005', 'G006', 'G007_36_B', 'G027A', 'G052', 'G062', 'G063',\n",
       "       'G255', 'G256', 'G257', 'S001', 'S002', 'S017', 'S018', 'S021', 'V001',\n",
       "       'V002', 'X001', 'X003R2', 'X025R', 'X028', 'X047R_EVS', 'X047R_WVS',\n",
       "       'X051'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Primary source of data: Gedeshi, Ilir, David Rotman, Merab Pachulia, Gevorg Poghosyan, and Sylvia Kritzinger. 2022. Joint EVS/WVS 2017–2022 Dataset, ZA7505 Data File Version 4.0.0. Cologne: GESIS. https://doi.org/10.4232/1.14320.\n",
    "#Coded as democracy from V-Dem version 2014: V-Dem [Country-Year/Country-Date] Dataset v14. Gothenburg: Varieties of Democracy (V-Dem) Project. https://doi.org/10.23696/mcwt-fr58.\n",
    "#Country income and size of immigrant population coded from World Bank. 2024.World Development Indicators.Washington, DC: World Bank. https://data.worldbank.org/products/wdi.\n",
    "ivs=pd.read_csv(thepath,low_memory=False,index_col=0)\n",
    "ivs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84936233-a555-464b-b7e4-09d77338653b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    653720.000000\n",
       "mean       2005.259987\n",
       "std          10.440017\n",
       "min        1981.000000\n",
       "25%        1998.000000\n",
       "50%        2007.000000\n",
       "75%        2013.000000\n",
       "max        2022.000000\n",
       "Name: Year, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coverage by time\n",
    "ivs.Year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bb5d24a-7431-4eef-9741-d124f0983e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coverage by geography\n",
    "len(ivs.Country.unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5879711-f7d8-4248-8a1a-f03c9ab5ab8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>iso3n</th>\n",
       "      <th>mostrecent</th>\n",
       "      <th>Region</th>\n",
       "      <th>subregion</th>\n",
       "      <th>cincome</th>\n",
       "      <th>cimmig</th>\n",
       "      <th>regime</th>\n",
       "      <th>pwght</th>\n",
       "      <th>...</th>\n",
       "      <th>V001</th>\n",
       "      <th>V002</th>\n",
       "      <th>X001</th>\n",
       "      <th>X003R2</th>\n",
       "      <th>X025R</th>\n",
       "      <th>X028</th>\n",
       "      <th>X047R_EVS</th>\n",
       "      <th>X047R_WVS</th>\n",
       "      <th>X051</th>\n",
       "      <th>survey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Country, Year, iso3n, mostrecent, Region, subregion, cincome, cimmig, regime, pwght, A124_06, C002, E033, G001, G005, G006, G007_36_B, G027A, G052, G062, G063, G255, G256, G257, S001, S002, S017, S018, S021, V001, V002, X001, X003R2, X025R, X028, X047R_EVS, X047R_WVS, X051, survey]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 39 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add wave information\n",
    "conditions=[\n",
    "    (ivs['Year']<1990)&(ivs['S001']==1)\n",
    " ,  (ivs['Year']>=1990)&(ivs['Year']<1999)&(ivs['S001']==1)\n",
    " ,  (ivs['Year']>=1999)&(ivs['Year']<2008)&(ivs['S001']==1)\n",
    " ,  (ivs['Year']>=2008)&(ivs['Year']<2017)&(ivs['S001']==1)\n",
    " ,  (ivs['Year']>=2017)&(ivs['S001']==1)\n",
    " , (ivs['S001']==2)&(ivs['S002']==1)\n",
    "    , (ivs['S001']==2)&(ivs['S002']==2)\n",
    "    , (ivs['S001']==2)&(ivs['S002']==3)\n",
    "    , (ivs['S001']==2)&(ivs['S002']==4)\n",
    "    , (ivs['S001']==2)&(ivs['S002']==5)\n",
    "    , (ivs['S001']==2)&(ivs['S002']==6)\n",
    "    , (ivs['S001']==2)&(ivs['S002']==7)\n",
    "    ]\n",
    "choices=['evs1981','evs1990','evs1999','evs2008','evs2017','wvs1','wvs2','wvs3','wvs4','wvs5','wvs6','wvs7']\n",
    "ivs['survey']=np.select(conditions,choices,default=None)\n",
    "ivs.loc[pd.isna(ivs['survey'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a4e8587-18e1-437f-ac8c-86d496c089ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support for pro-national hiring preferences\n",
    "# “When jobs are scarce, employers should give priority to people of this country over immigrants.”/\"Employers should give priority to (nation) people than immigrants\"\n",
    "# Three versions of the answers to this question are simplified by IVS into a three-level response\n",
    "# V1:\n",
    "# 1 Agree\n",
    "# 2 Disagree\n",
    "# 3 Neither\n",
    "# V2:\n",
    "# 1 Agree strongly <recoded to 1>\n",
    "# 2 Agree <recoded to 1>\n",
    "# 3 Neither agree nor disagree <>\n",
    "# 4 Disagree <recoded to 2>\n",
    "# 5 Disagree strongly <recoded to 2>\n",
    "# V3:\n",
    "# 1 Agree strongly <recoded to 1>\n",
    "# 2 Agree <recoded to 1>\n",
    "# 3 Neither agree nor disagree <>\n",
    "# 4 Disagree <recoded to 2>\n",
    "# 5 Disagree strongly <recoded to 2>\n",
    "conditions=[\n",
    "    (ivs['C002']==1),\n",
    "    (ivs['C002']==2),\n",
    "    (ivs['C002']==3)\n",
    "]\n",
    "choices=['Agree','Disagree','Neutral']\n",
    "ivs['jobs'] = np.select(conditions,choices,default=None)\n",
    "ivs['jobs'] = ivs['jobs'].astype('category')\n",
    "ivs['jobs'] = ivs.jobs.cat.set_categories(\n",
    "     new_categories = [\"Agree\",\"Neutral\",\"Disagree\"], ordered = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f019da9-38d0-488c-b4ab-df817d6bc3e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sex\n",
    "ivs['sex']=ivs['X001'].map({1:'Male',2:'Female'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb31161d-2125-459c-9759-7a887b8cb6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Age\n",
    "ivs['age']=ivs['X003R2'].map({1:'15-29',2:'30-49',3:'50+'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab1e4030-8735-4421-b33d-261391a96d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Education\n",
    "ivs['edu']=ivs['X025R'].map({1:'Lower education',2:'Intermediate education',3:'Higher education'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e82daf8f-1ad1-408b-acf8-2f01b20d6623",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Income variables\n",
    "ivs['income']=ivs['X047R_WVS'].map({1:'Lower income',2:'Intermediate income',3:'Higher income'})\n",
    "ivs.loc[ivs['X047R_EVS']==1,'income']='Lower income'\n",
    "ivs.loc[ivs['X047R_EVS']==2,'income']='Intermediate income'\n",
    "ivs.loc[ivs['X047R_EVS']==3,'income']='Higher income'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e4d5364b-edd9-436b-8f30-4e4722cb2e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Employment - X028\n",
    "# 1 Full time (30h a week or more)\n",
    "# 2 Part time (less then 30 hours a week)\n",
    "# 3 Self employed\n",
    "# 4 Retired/pensioned\n",
    "# 5 Housewife (not otherwise employed)\n",
    "# 6 Student\n",
    "# 7 Unemployed\n",
    "# 8 Other\n",
    "conditions=[\n",
    "    (ivs.X028<=3),\n",
    "    (ivs.X028==7),\n",
    "       (ivs.X028.isin([4,5,6,8]))\n",
    "]\n",
    "choices=['Employed','Unemployed','Not in workforce']\n",
    "ivs['employment']=np.select(conditions,choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8f0cd1ec-0442-4d97-baa3-649a1efda2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ideology\n",
    "#``In political matters, people talk of 'the left' and 'the right'. How would you place your views on this scale generally speaking?'' Scale 1 (left) to 10 (right). ``Left ideology'' indicates respondent chose a five or below. ``Right ideology'' indicates they chose six or higher.\n",
    "conditions=[\n",
    "    ivs.E033<=5,\n",
    "    ivs.E033>=6\n",
    "]\n",
    "choices=['Left','Right']\n",
    "ivs['ideology']=np.select(conditions,choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "33a6d481-b837-441c-b9d7-30687454e1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place identity\n",
    "#G001 - Which of these geographical groups would you say you belong to first of all?\n",
    "# -5 Missing; Unknown\n",
    "# -4 Not asked in survey\n",
    "# -3 Not applicable\n",
    "# -2 No answer\n",
    "# -1 Don't know\n",
    "# 1 Locality or town where you live\n",
    "# 2 Region or country where you live\n",
    "# 3 Country as a whole\n",
    "# 4 Europe\n",
    "# 5 The world as a whole\n",
    "#G062 - People have different views about themselves and how they relate to the world. \n",
    "#Using this card, would you tell me how close do you feel to…?, - asked all regions\n",
    "    # [Continent; e.g. Europe],\n",
    "    # Response categories in EVS Trend File:,\n",
    "    # -5 Missing: Other,\n",
    "    # -4 Not asked in survey,\n",
    "    # -3 Not applicable,\n",
    "    # -2 No answer,\n",
    "    # -1 Don´t know,\n",
    "    # 1 Very close,\n",
    "    # 2 Close,\n",
    "    # 3 Not very close,\n",
    "    # 4 Not close at all,\n",
    "    #G063 - World,\n",
    "    #G255 - Village, town or city,\n",
    "    #G256 - region or district,\n",
    "    #G257 - country,\n",
    "    #ivs['Max'] = ivs[['G255','G256']].idxmin(axis=1)\n",
    "ivs['subntnlmin'] = ivs[['G255','G256']].min(axis=1)\n",
    "ivs['intlmin'] = ivs[['G063','G062']].min(axis=1)\n",
    "ivs['placemin']=ivs[['subntnlmin','intlmin','G257']].min(axis=1)\n",
    "conditions=[\n",
    "    (ivs['placemin']==4), #close nowhere\n",
    "    (ivs['placemin']==3)&(ivs['placemin']==ivs['subntnlmin'])&(ivs['placemin']==ivs['G257'])&(ivs['placemin']==ivs['intlmin']), #close nowhere\n",
    "    (ivs['placemin']<3)&(ivs['placemin']==ivs['subntnlmin'])&(ivs['placemin']==ivs['G257'])&(ivs['placemin']==ivs['intlmin']), #closest all\n",
    "    (ivs['placemin']!=4)&(ivs['placemin']==ivs['subntnlmin'])&(ivs['placemin']!=ivs['G257'])&(ivs['placemin']!=ivs['intlmin']), #closest sub   \n",
    "    (ivs['placemin']!=4)&(ivs['placemin']==ivs['subntnlmin'])&(ivs['placemin']==ivs['G257'])&(ivs['placemin']!=ivs['intlmin']), #closest sub + cntry\n",
    "    (ivs['placemin']!=4)&(ivs['placemin']==ivs['subntnlmin'])&(ivs['placemin']!=ivs['G257'])&(ivs['placemin']==ivs['intlmin']), #closest sub + intl\n",
    "    (ivs['placemin']!=4)&(ivs['placemin']!=ivs['subntnlmin'])&(ivs['placemin']==ivs['G257'])&(ivs['placemin']!=ivs['intlmin']), #closest cntry\n",
    "    (ivs['placemin']!=4)&(ivs['placemin']!=ivs['subntnlmin'])&(ivs['placemin']==ivs['G257'])&(ivs['placemin']==ivs['intlmin']), #closest cntry and intl\n",
    "   (ivs['placemin']!=4)&(ivs['placemin']!=ivs['subntnlmin'])&(ivs['placemin']!=ivs['G257'])&(ivs['placemin']==ivs['intlmin']), #cintl\n",
    "    (ivs.G001==1)|(ivs.G001==2), #close sub\n",
    "    (ivs.G001==3), #close ntnl\n",
    "    (ivs.G001>3) #close intl\n",
    "]\n",
    "choices=[\n",
    "    'Close nowhere','Close nowhere','Equally close all','Closest sub-national',\n",
    "    'Closest sub-national and national','Closest subnational and international','Closest national','Closest national and international','Closest international',\n",
    "    'Closest sub-national',\n",
    "    'Closest national',\n",
    "    'Closest international'\n",
    "]\n",
    "ivs['placeid']=np.select(conditions,choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "600673f6-a953-40c3-b748-c0ab9b858c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perceived impact of immigration\n",
    "# Now we would like to know your opinion about the people from other countries who come to live in\n",
    "# [your country] - the immigrants. How would you evaluate the impact of these people on the\n",
    "# development of [your country]?\n",
    "conditions=[\n",
    "    (ivs['G052']<=2) &(ivs['G052']>0) #1 & 2 = Very and quite bad\n",
    "    , (ivs['G052']==3) #Neutral\n",
    "    , (ivs['G052']>=4) # 4 & 5 = Very and quite good\n",
    "]\n",
    "choices=['Bad','Neutral','Good']\n",
    "ivs['impact'] = np.select(conditions,choices,default=None)\n",
    "ivs['impact'] = ivs['impact'].astype('category')\n",
    "ivs['impact'] = ivs.impact.cat.set_categories(\n",
    "     new_categories = choices, ordered = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85a3d41d-32ff-4e06-a927-2daf3dbcef82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trust in foreigners\n",
    "# I would like to ask you how much you trust people from various groups. Could you tell me for each whether you trust people from this group\n",
    "# completely, somewhat, not very much or not at all?\n",
    "# People of another nationality\n",
    "# Response categories in EVS Trend File:\n",
    "# -5 Missing: Other\n",
    "# -4 Not asked in survey\n",
    "# -3 Not applicable\n",
    "# -2 No answer\n",
    "# -1 Don´t know\n",
    "# 1 Trust completely\n",
    "# 2 Trust somewhat\n",
    "# 3 Do not trust very much\n",
    "# 4 Do not trust at all\n",
    "conditions = [\n",
    "      (ivs['G007_36_B']==1),\n",
    "      (ivs['G007_36_B']==2),\n",
    "      (ivs['G007_36_B']==3),\n",
    "      (ivs['G007_36_B']==4)\n",
    "    ]\n",
    "choices  = [\"Trust foreigners\",\"Trust foreigners\",\"Don't trust foreigners\",\"Don't trust foreigners\"]\n",
    "ivs['trust_foreigners'] = np.select(conditions, choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7a506d43-ffd6-4514-801d-b69cad357529",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Foreign neighbors\n",
    "#On this list are various groups of people. Could you identify any that you would not like to have as neighbours? Immigrants and foreign workers\n",
    "conditions=[\n",
    "    (ivs['A124_06']==0), \n",
    "    (ivs['A124_06']==1) \n",
    "]\n",
    "choices=['Foreign neighbor OK','Foreign neighbor not OK']\n",
    "ivs['foreignneighborsOK']=np.select(conditions,choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07afee8e-ee51-4427-bf4b-d4daa2fc5c58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# National pride\n",
    "# G006\n",
    "# Master question in EVS 1990 (ZA4460, Q650); EVS 1999 (ZA3811, Q71); EVS 2008 (ZA4800, Q72); EVS 2017 (ZA7500, Q47):\n",
    "# <If respondent is a national of [COUNTRY]>\n",
    "# How proud are you to be a [COUNTRY] national?\n",
    "# -5 Missing: Other\n",
    "# -4 Not asked in survey\n",
    "# -3 Not applicable; Foreigner; Has not [country] nationality\n",
    "# -2 No answer\n",
    "# -1 Don´t know\n",
    "# 1 Very proud\n",
    "# 2 Quite proud\n",
    "# 3 Not very proud\n",
    "# 4 Not at all proud\n",
    "conditions = [\n",
    "  (ivs['G006']==1)|(ivs['G006']==2)\n",
    ", (ivs['G006']==3)|(ivs['G006']==4)\n",
    "]\n",
    "choices  = [\n",
    "      \"Proud of nationality\"\n",
    "    , 'Not proud of nationality'\n",
    "]\n",
    "ivs['ntnlpride'] = np.select(conditions, choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "742edc00-05af-41d5-a9d9-70f69df38a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ethnicity information - code membership in largest ethnic group\n",
    "ivs.loc[ivs.X051==\".\",'X051']=np.nan\n",
    "ivs['ethnic']=pd.to_numeric(ivs['X051'])\n",
    "\n",
    "df=ivs.loc[(ivs.ethnic>0),['Country','Year','ethnic']].copy().groupby(['Year','Country'])['ethnic'].agg(pd.Series.mode).reset_index()\n",
    "df.rename(columns={'ethnic':'mostcommon_ethnic'},inplace=True)\n",
    "\n",
    "ivs=ivs.merge(df,how='left')\n",
    "conditions=[\n",
    "     ivs['ethnic']==ivs['mostcommon_ethnic'],\n",
    "     ivs['ethnic']!=ivs['mostcommon_ethnic']\n",
    "]\n",
    "choices=['Ethnic plurality','Ethnic minority']\n",
    "ivs['ethnicplu']=np.select(conditions,choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f6c75f19-b704-40d5-93e3-a658b8abfd07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "national\n",
       "National        163480\n",
       "Non-national      6644\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Nationality\n",
    "# G005\n",
    "# Are you a national of [COUNTRY]?\n",
    "# Master question in EVS 2017 (ZA7500, Q46):\n",
    "# Do you have [COUNTRY’S] nationality?\n",
    "# -5 Missing: Other\n",
    "# -4 Not asked in survey\n",
    "# -3 Not applicable\n",
    "# -2 No answer\n",
    "# -1 Don't know\n",
    "# 0 No\n",
    "# 1 Yes\n",
    "conditions = [\n",
    "  (ivs['G005']==1)\n",
    ", (ivs['G005']==0)\n",
    "]\n",
    "choices  = ['National','Non-national']\n",
    "ivs['national'] = np.select(conditions, choices,default=None)\n",
    "ivs.national.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "85f3b68a-ecae-4946-9d8a-7022df4e014e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Immigrant generation\n",
    "#G027A - Respondent immigrant / born in country\n",
    "# 1 I am born in this country\n",
    "# 2 I am an immigrant to this country\n",
    "#V002 - Mother born in country\n",
    "# 0 No\n",
    "# 1 Yes\n",
    "#V001 - Father born in country\n",
    "# 0 No\n",
    "# 1 Yes\n",
    "conditions =  [\n",
    "    (ivs['G027A']==2) # \n",
    "    , (ivs['G027A']==1)&(ivs['V001']==0)\n",
    "    , (ivs['G027A']==1)&(ivs['V002']==0)\n",
    "    , (ivs['G027A']==1)&(ivs['V001']==1)&(ivs['V002']==1)\n",
    "    , (ivs['G027A']==1)&(ivs['V001']==1)&(pd.isna(ivs['V002']))\n",
    "    , (ivs['G027A']==1)&(ivs['V002']==1)&(pd.isna(ivs['V001']))\n",
    "]\n",
    "choices = ['Born abroad'\n",
    "           ,'Immigrant parent(s)'\n",
    "           ,'Immigrant parent(s)'\n",
    "           ,'Local parent(s)'\n",
    "         ,'Local parent(s)'\n",
    "     ,'Local parent(s)'\n",
    "           ]\n",
    "ivs['immigstatus']=np.select(conditions,choices,default=None)\n",
    "choices=['Born abroad','Born locally','Born locally','Born locally','Born locally','Born locally']\n",
    "ivs['bpl']=np.select(conditions,choices,default=None)\n",
    "\n",
    "conditions=[\n",
    "    (ivs['national']=='National')&(ivs['immigstatus']=='Born abroad') #ntrlzd\n",
    "    ,(ivs['national']=='National')&(pd.isna(ivs['immigstatus']))&(ivs['G027A']==2) #Ntrlzd\n",
    "    ,(ivs['national']=='National')&(ivs['immigstatus']!='Born abroad') #locally born national\n",
    "    ,(ivs['national']=='National')&(pd.isna(ivs['immigstatus']))&(ivs['G027A']==1) #locally born national\n",
    "    ,(ivs['national']=='Non-national')\n",
    "]\n",
    "choices=['Naturalized','Naturalized',\n",
    "         'Locally-born national','Locally-born national','Non-national']\n",
    "ivs['ntrlzd']=np.select(conditions,choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "01250f23-ce37-437f-812d-b0e3fcebc1ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combine ethnicity and nationality/immigration status information\n",
    "for i in ['national','ntrlzd','immigstatus','bpl']:\n",
    "    ivs[i+'Xeth']=ivs[i].astype(str)+', '+ivs['ethnicplu'].astype(str)\n",
    "    ivs.loc[((pd.isna(ivs[i]))|(pd.isna(ivs['ethnicplu']))),i+'Xeth']=np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1bf3ee-1a41-4909-a753-7bd87979d74b",
   "metadata": {},
   "source": [
    "#### Most recent jobs survey by country (Figure 2.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e536fac5-0dd9-4688-8228-f7768caa3063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>iso3n</th>\n",
       "      <th>group_count</th>\n",
       "      <th>pc_Agree</th>\n",
       "      <th>pc_Neutral</th>\n",
       "      <th>pc_Disagree</th>\n",
       "      <th>sortvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>818</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>97.466216</td>\n",
       "      <td>1.604730</td>\n",
       "      <td>0.929054</td>\n",
       "      <td>99.070946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>368</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>89.447236</td>\n",
       "      <td>9.296482</td>\n",
       "      <td>1.256281</td>\n",
       "      <td>98.743719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>91.579357</td>\n",
       "      <td>6.328429</td>\n",
       "      <td>2.092214</td>\n",
       "      <td>97.907786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>422</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>91.083333</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>97.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>800</td>\n",
       "      <td>994.0</td>\n",
       "      <td>92.314389</td>\n",
       "      <td>4.777630</td>\n",
       "      <td>2.907981</td>\n",
       "      <td>97.092019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>788</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>89.800995</td>\n",
       "      <td>6.882255</td>\n",
       "      <td>3.316750</td>\n",
       "      <td>96.683250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>400</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>93.261231</td>\n",
       "      <td>3.244592</td>\n",
       "      <td>3.494176</td>\n",
       "      <td>96.505824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>440</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>86.795321</td>\n",
       "      <td>9.660585</td>\n",
       "      <td>3.544095</td>\n",
       "      <td>96.455905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>458</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>86.519421</td>\n",
       "      <td>9.596344</td>\n",
       "      <td>3.884235</td>\n",
       "      <td>96.115765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>703</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>86.980949</td>\n",
       "      <td>9.076365</td>\n",
       "      <td>3.942686</td>\n",
       "      <td>96.057314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>104</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>95.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>410</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>77.911647</td>\n",
       "      <td>17.510040</td>\n",
       "      <td>4.578313</td>\n",
       "      <td>95.421687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Libya</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>434</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>91.351805</td>\n",
       "      <td>4.030227</td>\n",
       "      <td>4.617968</td>\n",
       "      <td>95.382032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>87.230255</td>\n",
       "      <td>8.046855</td>\n",
       "      <td>4.722890</td>\n",
       "      <td>95.277110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>499</td>\n",
       "      <td>986.0</td>\n",
       "      <td>78.558766</td>\n",
       "      <td>16.675846</td>\n",
       "      <td>4.765388</td>\n",
       "      <td>95.234612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Malta</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>470</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>91.905143</td>\n",
       "      <td>2.668516</td>\n",
       "      <td>5.426341</td>\n",
       "      <td>94.573659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>84.510835</td>\n",
       "      <td>9.977158</td>\n",
       "      <td>5.512006</td>\n",
       "      <td>94.487994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>586</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>86.984925</td>\n",
       "      <td>7.185930</td>\n",
       "      <td>5.829146</td>\n",
       "      <td>94.170854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>332</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>58.548960</td>\n",
       "      <td>35.616438</td>\n",
       "      <td>5.834602</td>\n",
       "      <td>94.165398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>887</td>\n",
       "      <td>977.0</td>\n",
       "      <td>87.512794</td>\n",
       "      <td>6.550665</td>\n",
       "      <td>5.936540</td>\n",
       "      <td>94.063460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>462</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>88.931665</td>\n",
       "      <td>5.101059</td>\n",
       "      <td>5.967276</td>\n",
       "      <td>94.032724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>762</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>85.416667</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>93.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Mali</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>466</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>83.781965</td>\n",
       "      <td>9.084791</td>\n",
       "      <td>7.133244</td>\n",
       "      <td>92.866756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>50</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>87.183811</td>\n",
       "      <td>5.564924</td>\n",
       "      <td>7.251265</td>\n",
       "      <td>92.748735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Iran</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>364</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>90.727151</td>\n",
       "      <td>2.001334</td>\n",
       "      <td>7.271514</td>\n",
       "      <td>92.728486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>348</td>\n",
       "      <td>1488.0</td>\n",
       "      <td>84.394221</td>\n",
       "      <td>8.196180</td>\n",
       "      <td>7.409599</td>\n",
       "      <td>92.590401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>807</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>82.679738</td>\n",
       "      <td>9.905597</td>\n",
       "      <td>7.414665</td>\n",
       "      <td>92.585335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Taiwan ROC</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>158</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>87.568441</td>\n",
       "      <td>5.012867</td>\n",
       "      <td>7.418692</td>\n",
       "      <td>92.581308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>688</td>\n",
       "      <td>1476.0</td>\n",
       "      <td>73.147941</td>\n",
       "      <td>19.011412</td>\n",
       "      <td>7.840648</td>\n",
       "      <td>92.159352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>268</td>\n",
       "      <td>2178.0</td>\n",
       "      <td>87.230392</td>\n",
       "      <td>4.750861</td>\n",
       "      <td>8.018747</td>\n",
       "      <td>91.981253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>704</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>87.166667</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>91.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>428</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>73.362367</td>\n",
       "      <td>18.206401</td>\n",
       "      <td>8.431232</td>\n",
       "      <td>91.568768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>634</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>86.027400</td>\n",
       "      <td>5.360001</td>\n",
       "      <td>8.612598</td>\n",
       "      <td>91.387402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>392</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>62.193252</td>\n",
       "      <td>29.064417</td>\n",
       "      <td>8.742331</td>\n",
       "      <td>91.257669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Country    Year  iso3n  group_count   pc_Agree  \\\n",
       "25                    Egypt  2018.0    818       1184.0  97.466216   \n",
       "42                     Iraq  2018.0    368       1194.0  89.447236   \n",
       "6                Azerbaijan  2018.0     31       1755.0  91.579357   \n",
       "52                  Lebanon  2018.0    422       1200.0  91.083333   \n",
       "98                   Uganda  2001.0    800        994.0  92.314389   \n",
       "96                  Tunisia  2019.0    788       1206.0  89.800995   \n",
       "46                   Jordan  2018.0    400       1202.0  93.261231   \n",
       "54                Lithuania  2018.0    440       1435.0  86.795321   \n",
       "56                 Malaysia  2018.0    458       1313.0  86.519421   \n",
       "84                 Slovakia  2022.0    703       1196.0  86.980949   \n",
       "65                  Myanmar  2020.0    104       1200.0  93.333333   \n",
       "87              South Korea  2018.0    410       1245.0  77.911647   \n",
       "53                    Libya  2022.0    434       1191.0  91.351805   \n",
       "11   Bosnia and Herzegovina  2019.0     70       1713.0  87.230255   \n",
       "63               Montenegro  2019.0    499        986.0  78.558766   \n",
       "59                    Malta  2008.0    470       1490.0  91.905143   \n",
       "13                 Bulgaria  2017.0    100       1537.0  84.510835   \n",
       "72                 Pakistan  2018.0    586       1990.0  86.984925   \n",
       "36                    Haiti  2016.0    332       1971.0  58.548960   \n",
       "105                   Yemen  2014.0    887        977.0  87.512794   \n",
       "57                 Maldives  2021.0    462       1039.0  88.931665   \n",
       "92               Tajikistan  2020.0    762       1200.0  85.416667   \n",
       "58                     Mali  2007.0    466       1486.0  83.781965   \n",
       "7                Bangladesh  2018.0     50       1186.0  87.183811   \n",
       "41                     Iran  2020.0    364       1499.0  90.727151   \n",
       "37                  Hungary  2018.0    348       1488.0  84.394221   \n",
       "70          North Macedonia  2019.0    807       1099.0  82.679738   \n",
       "91               Taiwan ROC  2019.0    158       1221.0  87.568441   \n",
       "82                   Serbia  2018.0    688       1476.0  73.147941   \n",
       "30                  Georgia  2018.0    268       2178.0  87.230392   \n",
       "104                 Vietnam  2020.0    704       1200.0  87.166667   \n",
       "51                   Latvia  2021.0    428       1311.0  73.362367   \n",
       "77                    Qatar  2010.0    634       1057.0  86.027400   \n",
       "45                    Japan  2019.0    392       1304.0  62.193252   \n",
       "\n",
       "     pc_Neutral  pc_Disagree  sortvalue  \n",
       "25     1.604730     0.929054  99.070946  \n",
       "42     9.296482     1.256281  98.743719  \n",
       "6      6.328429     2.092214  97.907786  \n",
       "52     6.083333     2.833333  97.166667  \n",
       "98     4.777630     2.907981  97.092019  \n",
       "96     6.882255     3.316750  96.683250  \n",
       "46     3.244592     3.494176  96.505824  \n",
       "54     9.660585     3.544095  96.455905  \n",
       "56     9.596344     3.884235  96.115765  \n",
       "84     9.076365     3.942686  96.057314  \n",
       "65     2.166667     4.500000  95.500000  \n",
       "87    17.510040     4.578313  95.421687  \n",
       "53     4.030227     4.617968  95.382032  \n",
       "11     8.046855     4.722890  95.277110  \n",
       "63    16.675846     4.765388  95.234612  \n",
       "59     2.668516     5.426341  94.573659  \n",
       "13     9.977158     5.512006  94.487994  \n",
       "72     7.185930     5.829146  94.170854  \n",
       "36    35.616438     5.834602  94.165398  \n",
       "105    6.550665     5.936540  94.063460  \n",
       "57     5.101059     5.967276  94.032724  \n",
       "92     8.333333     6.250000  93.750000  \n",
       "58     9.084791     7.133244  92.866756  \n",
       "7      5.564924     7.251265  92.748735  \n",
       "41     2.001334     7.271514  92.728486  \n",
       "37     8.196180     7.409599  92.590401  \n",
       "70     9.905597     7.414665  92.585335  \n",
       "91     5.012867     7.418692  92.581308  \n",
       "82    19.011412     7.840648  92.159352  \n",
       "30     4.750861     8.018747  91.981253  \n",
       "104    4.500000     8.333333  91.666667  \n",
       "51    18.206401     8.431232  91.568768  \n",
       "77     5.360001     8.612598  91.387402  \n",
       "45    29.064417     8.742331  91.257669  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent=ivs.loc[ivs['mostrecent']==1].copy()\n",
    "jobs2=ivs_table('jobs',['Country','Year','iso3n'],recent)\n",
    "jobs2['sortvalue']=jobs2['pc_Agree']+jobs2['pc_Neutral']\n",
    "jobs2.sort_values(['sortvalue'],ascending=False).head(34)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e77b8e3d-0840-4a17-a0dd-cb7bae30d163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>iso3n</th>\n",
       "      <th>group_count</th>\n",
       "      <th>pc_Agree</th>\n",
       "      <th>pc_Neutral</th>\n",
       "      <th>pc_Disagree</th>\n",
       "      <th>sortvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>32</td>\n",
       "      <td>977.0</td>\n",
       "      <td>63.496954</td>\n",
       "      <td>17.001106</td>\n",
       "      <td>19.501939</td>\n",
       "      <td>80.498061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>191</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>70.576986</td>\n",
       "      <td>9.795968</td>\n",
       "      <td>19.627046</td>\n",
       "      <td>80.372954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>646</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>52.783235</td>\n",
       "      <td>26.457105</td>\n",
       "      <td>20.759659</td>\n",
       "      <td>79.240341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>558</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>67.833333</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>20.916667</td>\n",
       "      <td>79.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>152</td>\n",
       "      <td>994.0</td>\n",
       "      <td>57.718407</td>\n",
       "      <td>20.741262</td>\n",
       "      <td>21.540332</td>\n",
       "      <td>78.459668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>710</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>50.644750</td>\n",
       "      <td>27.723841</td>\n",
       "      <td>21.631409</td>\n",
       "      <td>78.368591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>620</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>60.085034</td>\n",
       "      <td>18.063529</td>\n",
       "      <td>21.851437</td>\n",
       "      <td>78.148563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>231</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>74.409128</td>\n",
       "      <td>3.504482</td>\n",
       "      <td>22.086390</td>\n",
       "      <td>77.913610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>76</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>64.066298</td>\n",
       "      <td>13.253112</td>\n",
       "      <td>22.680591</td>\n",
       "      <td>77.319409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>214</td>\n",
       "      <td>380.0</td>\n",
       "      <td>46.052632</td>\n",
       "      <td>30.789474</td>\n",
       "      <td>23.157895</td>\n",
       "      <td>76.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>170</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>68.881579</td>\n",
       "      <td>7.039474</td>\n",
       "      <td>24.078947</td>\n",
       "      <td>75.921053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>372</td>\n",
       "      <td>977.0</td>\n",
       "      <td>67.644730</td>\n",
       "      <td>8.103368</td>\n",
       "      <td>24.251902</td>\n",
       "      <td>75.748098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>862</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>64.369748</td>\n",
       "      <td>9.663866</td>\n",
       "      <td>25.966387</td>\n",
       "      <td>74.033613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>484</td>\n",
       "      <td>1727.0</td>\n",
       "      <td>50.205646</td>\n",
       "      <td>21.162821</td>\n",
       "      <td>28.631533</td>\n",
       "      <td>71.368467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>40</td>\n",
       "      <td>1617.0</td>\n",
       "      <td>51.644996</td>\n",
       "      <td>18.700613</td>\n",
       "      <td>29.654391</td>\n",
       "      <td>70.345609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>United States</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>840</td>\n",
       "      <td>2589.0</td>\n",
       "      <td>45.316025</td>\n",
       "      <td>23.997921</td>\n",
       "      <td>30.686054</td>\n",
       "      <td>69.313946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>50.363418</td>\n",
       "      <td>18.075063</td>\n",
       "      <td>31.561519</td>\n",
       "      <td>68.438481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>554</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>47.258225</td>\n",
       "      <td>20.837488</td>\n",
       "      <td>31.904287</td>\n",
       "      <td>68.095713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Finland</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>246</td>\n",
       "      <td>1182.0</td>\n",
       "      <td>48.007037</td>\n",
       "      <td>19.837319</td>\n",
       "      <td>32.155644</td>\n",
       "      <td>67.844356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>858</td>\n",
       "      <td>976.0</td>\n",
       "      <td>59.685965</td>\n",
       "      <td>7.726496</td>\n",
       "      <td>32.587539</td>\n",
       "      <td>67.412461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>860</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>45.827538</td>\n",
       "      <td>21.349096</td>\n",
       "      <td>32.823366</td>\n",
       "      <td>67.176634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>124</td>\n",
       "      <td>4018.0</td>\n",
       "      <td>36.875928</td>\n",
       "      <td>29.235637</td>\n",
       "      <td>33.888436</td>\n",
       "      <td>66.111564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>724</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>43.541159</td>\n",
       "      <td>16.737608</td>\n",
       "      <td>39.721233</td>\n",
       "      <td>60.278767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>France</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>250</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>43.194812</td>\n",
       "      <td>16.315865</td>\n",
       "      <td>40.489322</td>\n",
       "      <td>59.510678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>56</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>52.158330</td>\n",
       "      <td>6.713333</td>\n",
       "      <td>41.128337</td>\n",
       "      <td>58.871663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>208</td>\n",
       "      <td>3343.0</td>\n",
       "      <td>35.625992</td>\n",
       "      <td>23.238545</td>\n",
       "      <td>41.135463</td>\n",
       "      <td>58.864537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>352</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>28.994920</td>\n",
       "      <td>27.058712</td>\n",
       "      <td>43.946368</td>\n",
       "      <td>56.053632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>442</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>40.767135</td>\n",
       "      <td>14.733583</td>\n",
       "      <td>44.499282</td>\n",
       "      <td>55.500718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>528</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>32.955106</td>\n",
       "      <td>22.348298</td>\n",
       "      <td>44.696596</td>\n",
       "      <td>55.303404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Great Britain</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>826</td>\n",
       "      <td>2572.0</td>\n",
       "      <td>29.644411</td>\n",
       "      <td>20.094031</td>\n",
       "      <td>50.261558</td>\n",
       "      <td>49.738442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>20</td>\n",
       "      <td>998.0</td>\n",
       "      <td>28.256513</td>\n",
       "      <td>19.639279</td>\n",
       "      <td>52.104208</td>\n",
       "      <td>47.895792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>276</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>27.453581</td>\n",
       "      <td>17.904509</td>\n",
       "      <td>54.641910</td>\n",
       "      <td>45.358090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Norway</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>578</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>32.461182</td>\n",
       "      <td>11.934362</td>\n",
       "      <td>55.604456</td>\n",
       "      <td>44.395544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>752</td>\n",
       "      <td>1173.0</td>\n",
       "      <td>11.479708</td>\n",
       "      <td>16.028022</td>\n",
       "      <td>72.492270</td>\n",
       "      <td>27.507730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Country    Year  iso3n  group_count   pc_Agree  pc_Neutral  \\\n",
       "2             Argentina  2017.0     32        977.0  63.496954   17.001106   \n",
       "19              Croatia  2017.0    191       1467.0  70.576986    9.795968   \n",
       "80               Rwanda  2012.0    646       1527.0  52.783235   26.457105   \n",
       "68            Nicaragua  2020.0    558       1200.0  67.833333   11.250000   \n",
       "16                Chile  2018.0    152        994.0  57.718407   20.741262   \n",
       "86         South Africa  2013.0    710       3483.0  50.644750   27.723841   \n",
       "76             Portugal  2020.0    620       1192.0  60.085034   18.063529   \n",
       "27             Ethiopia  2020.0    231       1227.0  74.409128    3.504482   \n",
       "12               Brazil  2018.0     76       1728.0  64.066298   13.253112   \n",
       "23   Dominican Republic  1996.0    214        380.0  46.052632   30.789474   \n",
       "18             Colombia  2018.0    170       1520.0  68.881579    7.039474   \n",
       "43              Ireland  2008.0    372        977.0  67.644730    8.103368   \n",
       "103           Venezuela  2021.0    862       1190.0  64.369748    9.663866   \n",
       "60               Mexico  2018.0    484       1727.0  50.205646   21.162821   \n",
       "5               Austria  2018.0     40       1617.0  51.644996   18.700613   \n",
       "100       United States  2017.0    840       2589.0  45.316025   23.997921   \n",
       "4             Australia  2018.0     36       1794.0  50.363418   18.075063   \n",
       "67          New Zealand  2020.0    554       1003.0  47.258225   20.837488   \n",
       "28              Finland  2017.0    246       1182.0  48.007037   19.837319   \n",
       "101             Uruguay  2022.0    858        976.0  59.685965    7.726496   \n",
       "102          Uzbekistan  2011.0    860       1438.0  45.827538   21.349096   \n",
       "15               Canada  2020.0    124       4018.0  36.875928   29.235637   \n",
       "88                Spain  2017.0    724       1200.0  43.541159   16.737608   \n",
       "29               France  2018.0    250       1850.0  43.194812   16.315865   \n",
       "9               Belgium  2009.0     56       1503.0  52.158330    6.713333   \n",
       "22              Denmark  2017.0    208       3343.0  35.625992   23.238545   \n",
       "38              Iceland  2017.0    352       1606.0  28.994920   27.058712   \n",
       "55           Luxembourg  2008.0    442       1578.0  40.767135   14.733583   \n",
       "66          Netherlands  2022.0    528       2027.0  32.955106   22.348298   \n",
       "33        Great Britain  2022.0    826       2572.0  29.644411   20.094031   \n",
       "1               Andorra  2018.0     20        998.0  28.256513   19.639279   \n",
       "31              Germany  2018.0    276       1508.0  27.453581   17.904509   \n",
       "71               Norway  2018.0    578       1116.0  32.461182   11.934362   \n",
       "89               Sweden  2017.0    752       1173.0  11.479708   16.028022   \n",
       "\n",
       "     pc_Disagree  sortvalue  \n",
       "2      19.501939  80.498061  \n",
       "19     19.627046  80.372954  \n",
       "80     20.759659  79.240341  \n",
       "68     20.916667  79.083333  \n",
       "16     21.540332  78.459668  \n",
       "86     21.631409  78.368591  \n",
       "76     21.851437  78.148563  \n",
       "27     22.086390  77.913610  \n",
       "12     22.680591  77.319409  \n",
       "23     23.157895  76.842105  \n",
       "18     24.078947  75.921053  \n",
       "43     24.251902  75.748098  \n",
       "103    25.966387  74.033613  \n",
       "60     28.631533  71.368467  \n",
       "5      29.654391  70.345609  \n",
       "100    30.686054  69.313946  \n",
       "4      31.561519  68.438481  \n",
       "67     31.904287  68.095713  \n",
       "28     32.155644  67.844356  \n",
       "101    32.587539  67.412461  \n",
       "102    32.823366  67.176634  \n",
       "15     33.888436  66.111564  \n",
       "88     39.721233  60.278767  \n",
       "29     40.489322  59.510678  \n",
       "9      41.128337  58.871663  \n",
       "22     41.135463  58.864537  \n",
       "38     43.946368  56.053632  \n",
       "55     44.499282  55.500718  \n",
       "66     44.696596  55.303404  \n",
       "33     50.261558  49.738442  \n",
       "1      52.104208  47.895792  \n",
       "31     54.641910  45.358090  \n",
       "71     55.604456  44.395544  \n",
       "89     72.492270  27.507730  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jobs2.sort_values(['sortvalue'],ascending=False).tail(34)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905554aa-bcd2-4bef-887f-db0f8bd76d8f",
   "metadata": {},
   "source": [
    "#### Answers to question re jobs preferences by region and country characteristics (Figure 2.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df1e2163-fa88-4d20-8710-1a09a7601b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>group_count</th>\n",
       "      <th>pc_Agree</th>\n",
       "      <th>pc_Neutral</th>\n",
       "      <th>pc_Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>22814.0</td>\n",
       "      <td>77.892868</td>\n",
       "      <td>9.516880</td>\n",
       "      <td>12.590252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americas</td>\n",
       "      <td>26109.0</td>\n",
       "      <td>63.129120</td>\n",
       "      <td>16.337980</td>\n",
       "      <td>20.532900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>50168.0</td>\n",
       "      <td>79.473940</td>\n",
       "      <td>10.718659</td>\n",
       "      <td>9.807401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>61960.0</td>\n",
       "      <td>62.142530</td>\n",
       "      <td>14.417807</td>\n",
       "      <td>23.439663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2797.0</td>\n",
       "      <td>48.844644</td>\n",
       "      <td>19.426186</td>\n",
       "      <td>31.729170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
       "0    Africa      22814.0  77.892868    9.516880    12.590252\n",
       "1  Americas      26109.0  63.129120   16.337980    20.532900\n",
       "2      Asia      50168.0  79.473940   10.718659     9.807401\n",
       "3    Europe      61960.0  62.142530   14.417807    23.439663\n",
       "4   Oceania       2797.0  48.844644   19.426186    31.729170"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byregion=ivs_table(avar='jobs',groupers=['Region'],df=recent,useeqwght=True)\n",
    "byregion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "28cb8e1a-2c0e-4395-aacb-c9633049c64f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subregion</th>\n",
       "      <th>group_count</th>\n",
       "      <th>pc_Agree</th>\n",
       "      <th>pc_Neutral</th>\n",
       "      <th>pc_Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central and S America and Caribbean</td>\n",
       "      <td>17775.0</td>\n",
       "      <td>67.252366</td>\n",
       "      <td>14.500945</td>\n",
       "      <td>18.246690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central and South Asia</td>\n",
       "      <td>11868.0</td>\n",
       "      <td>76.723057</td>\n",
       "      <td>11.335306</td>\n",
       "      <td>11.941637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East and SE Asia</td>\n",
       "      <td>20002.0</td>\n",
       "      <td>79.424551</td>\n",
       "      <td>10.987442</td>\n",
       "      <td>9.588008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>13993.0</td>\n",
       "      <td>76.945433</td>\n",
       "      <td>12.305206</td>\n",
       "      <td>10.749361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Middle East</td>\n",
       "      <td>18298.0</td>\n",
       "      <td>80.990386</td>\n",
       "      <td>10.138923</td>\n",
       "      <td>8.870692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North Africa</td>\n",
       "      <td>4781.0</td>\n",
       "      <td>88.161392</td>\n",
       "      <td>5.937012</td>\n",
       "      <td>5.901596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>North America</td>\n",
       "      <td>8334.0</td>\n",
       "      <td>44.114899</td>\n",
       "      <td>24.809413</td>\n",
       "      <td>31.075688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2797.0</td>\n",
       "      <td>48.844644</td>\n",
       "      <td>19.426186</td>\n",
       "      <td>31.729170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>18033.0</td>\n",
       "      <td>74.437548</td>\n",
       "      <td>10.721492</td>\n",
       "      <td>14.840960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Western Europe</td>\n",
       "      <td>47967.0</td>\n",
       "      <td>57.398178</td>\n",
       "      <td>15.094898</td>\n",
       "      <td>27.506924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             subregion  group_count   pc_Agree  pc_Neutral  \\\n",
       "0  Central and S America and Caribbean      17775.0  67.252366   14.500945   \n",
       "1               Central and South Asia      11868.0  76.723057   11.335306   \n",
       "2                     East and SE Asia      20002.0  79.424551   10.987442   \n",
       "3                       Eastern Europe      13993.0  76.945433   12.305206   \n",
       "4                          Middle East      18298.0  80.990386   10.138923   \n",
       "5                         North Africa       4781.0  88.161392    5.937012   \n",
       "6                        North America       8334.0  44.114899   24.809413   \n",
       "7                              Oceania       2797.0  48.844644   19.426186   \n",
       "8                   Sub-Saharan Africa      18033.0  74.437548   10.721492   \n",
       "9                       Western Europe      47967.0  57.398178   15.094898   \n",
       "\n",
       "   pc_Disagree  \n",
       "0    18.246690  \n",
       "1    11.941637  \n",
       "2     9.588008  \n",
       "3    10.749361  \n",
       "4     8.870692  \n",
       "5     5.901596  \n",
       "6    31.075688  \n",
       "7    31.729170  \n",
       "8    14.840960  \n",
       "9    27.506924  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ivs_table(avar='jobs',groupers=['subregion'],df=recent,useeqwght=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8f26ebcd-ca1f-44ee-8f5b-dbf9837ea0ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cincome</th>\n",
       "      <th>group_count</th>\n",
       "      <th>pc_Agree</th>\n",
       "      <th>pc_Neutral</th>\n",
       "      <th>pc_Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Highest income</td>\n",
       "      <td>67708.0</td>\n",
       "      <td>58.836942</td>\n",
       "      <td>15.649639</td>\n",
       "      <td>25.513419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>36821.0</td>\n",
       "      <td>79.307237</td>\n",
       "      <td>9.043642</td>\n",
       "      <td>11.649122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lowest income</td>\n",
       "      <td>11264.0</td>\n",
       "      <td>72.667754</td>\n",
       "      <td>13.862991</td>\n",
       "      <td>13.469255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>48055.0</td>\n",
       "      <td>76.021203</td>\n",
       "      <td>12.158653</td>\n",
       "      <td>11.820144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cincome  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
       "0       Highest income      67708.0  58.836942   15.649639    25.513419\n",
       "1  Lower middle income      36821.0  79.307237    9.043642    11.649122\n",
       "2        Lowest income      11264.0  72.667754   13.862991    13.469255\n",
       "3  Upper middle income      48055.0  76.021203   12.158653    11.820144"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ivs_table(avar='jobs',groupers=['cincome'],df=recent,useeqwght=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "60442a95-935b-43b0-b124-0fa5a1c6636a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cimmig</th>\n",
       "      <th>group_count</th>\n",
       "      <th>pc_Agree</th>\n",
       "      <th>pc_Neutral</th>\n",
       "      <th>pc_Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Above median (4.5-13.5%)</td>\n",
       "      <td>47452.0</td>\n",
       "      <td>64.566439</td>\n",
       "      <td>15.126133</td>\n",
       "      <td>20.307428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Below median (1.3-4.5%)</td>\n",
       "      <td>37648.0</td>\n",
       "      <td>76.514238</td>\n",
       "      <td>11.062921</td>\n",
       "      <td>12.422842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Highest immigration (&gt;13.5%)</td>\n",
       "      <td>35699.0</td>\n",
       "      <td>58.028969</td>\n",
       "      <td>15.828307</td>\n",
       "      <td>26.142725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lowest immigration (&lt;1.3%)</td>\n",
       "      <td>41828.0</td>\n",
       "      <td>77.578501</td>\n",
       "      <td>10.263558</td>\n",
       "      <td>12.157941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         cimmig  group_count   pc_Agree  pc_Neutral  \\\n",
       "0      Above median (4.5-13.5%)      47452.0  64.566439   15.126133   \n",
       "1       Below median (1.3-4.5%)      37648.0  76.514238   11.062921   \n",
       "2  Highest immigration (>13.5%)      35699.0  58.028969   15.828307   \n",
       "3    Lowest immigration (<1.3%)      41828.0  77.578501   10.263558   \n",
       "\n",
       "   pc_Disagree  \n",
       "0    20.307428  \n",
       "1    12.422842  \n",
       "2    26.142725  \n",
       "3    12.157941  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ivs_table(avar='jobs',groupers=['cimmig'],df=recent,useeqwght=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "df1888b1-9cfa-4b1e-adbf-39025cb37b12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regime</th>\n",
       "      <th>group_count</th>\n",
       "      <th>pc_Agree</th>\n",
       "      <th>pc_Neutral</th>\n",
       "      <th>pc_Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autocracy</td>\n",
       "      <td>57606.0</td>\n",
       "      <td>77.980962</td>\n",
       "      <td>11.047428</td>\n",
       "      <td>10.971610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Democracy</td>\n",
       "      <td>106242.0</td>\n",
       "      <td>64.344001</td>\n",
       "      <td>14.178273</td>\n",
       "      <td>21.477725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      regime  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
       "0  Autocracy      57606.0  77.980962   11.047428    10.971610\n",
       "1  Democracy     106242.0  64.344001   14.178273    21.477725"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ivs_table(avar='jobs',groupers=['regime'],df=recent,useeqwght=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ed2492-aa80-4a2f-98a1-4811b5991a1a",
   "metadata": {},
   "source": [
    "#### Appendix table A.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a880ced3-fbdb-4e26-b765-ed79ae66d688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "      sex  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0  Female     308490.0  70.867690   11.357397    17.774913\n",
      "1    Male     277089.0  73.065667   10.320569    16.613764\n",
      "\n",
      "Cross-tabulation of sex and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 585315\n",
      " Number of observations: 585315\n",
      " Degrees of freedom: 585314.00\n",
      "\n",
      "    sex     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "Female    Agree   0.355901  0.000680  0.354569  0.357236\n",
      "Female Disagree   0.096375  0.000420  0.095554  0.097201\n",
      "Female  Neutral   0.059463  0.000343  0.058794  0.060139\n",
      "  Male    Agree   0.343295  0.000685  0.341953  0.344639\n",
      "  Male Disagree   0.090980  0.000416  0.090168  0.091798\n",
      "  Male  Neutral   0.053987  0.000329  0.053345  0.054636\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(2): 56.0962 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(2): 56.1155 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "317593e1-aa91-4f9a-9616-60eb9f25db01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "     age  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0  15-29     152082.0  69.641089   11.181921    19.176990\n",
      "1  30-49     228237.0  73.523112    9.968839    16.508049\n",
      "2    50+     204081.0  71.855542   11.706790    16.437668\n",
      "\n",
      "Cross-tabulation of age and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 584394\n",
      " Number of observations: 584394\n",
      " Degrees of freedom: 584393.00\n",
      "\n",
      "   age     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "15-29    Agree   0.186245  0.000564  0.185141  0.187353\n",
      "15-29 Disagree   0.051298  0.000327  0.050660  0.051943\n",
      "15-29  Neutral   0.031151  0.000263  0.030639  0.031670\n",
      "30-49    Agree   0.274114  0.000640  0.272861  0.275370\n",
      "30-49 Disagree   0.076011  0.000379  0.075271  0.076757\n",
      "30-49  Neutral   0.043944  0.000292  0.043375  0.044520\n",
      "  50+    Agree   0.238894  0.000604  0.237712  0.240079\n",
      "  50+ Disagree   0.060080  0.000333  0.059431  0.060736\n",
      "  50+  Neutral   0.038265  0.000277  0.037726  0.038811\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(4): 189.6514 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(4): 190.6391 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34e529a0-a748-4b38-8370-512a0dbdddc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "                      edu  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0        Higher education     149527.0  65.691368   13.106938    21.201694\n",
      "1  Intermediate education     235169.0  69.533766   12.888322    17.577911\n",
      "2         Lower education     145878.0  76.968823    8.174288    14.856889\n",
      "\n",
      "Cross-tabulation of edu and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 530323\n",
      " Number of observations: 530323\n",
      " Degrees of freedom: 530322.00\n",
      "\n",
      "                    edu     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "      Higher education    Agree   0.168415  0.000558  0.167324  0.169511\n",
      "      Higher education Disagree   0.062081  0.000353  0.061392  0.062778\n",
      "      Higher education  Neutral   0.034016  0.000262  0.033506  0.034534\n",
      "Intermediate education    Agree   0.310944  0.000689  0.309596  0.312296\n",
      "Intermediate education Disagree   0.079217  0.000409  0.078418  0.080023\n",
      "Intermediate education  Neutral   0.053982  0.000346  0.053308  0.054664\n",
      "       Lower education    Agree   0.220540  0.000647  0.219275  0.221810\n",
      "       Lower education Disagree   0.041637  0.000317  0.041019  0.042263\n",
      "       Lower education  Neutral   0.029168  0.000275  0.028634  0.029711\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(4): 5532.7438 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(4): 5494.9727 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('edu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "18af52c6-b9e0-4f18-8229-f112512e0341",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "                income  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0        Higher income      93987.0  66.975388   12.570171    20.454441\n",
      "1  Intermediate income     262290.0  70.809678   11.430798    17.759523\n",
      "2         Lower income     170443.0  76.160981    8.657258    15.181761\n",
      "\n",
      "Cross-tabulation of income and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 526540\n",
      " Number of observations: 526540\n",
      " Degrees of freedom: 526539.00\n",
      "\n",
      "              income     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "      Higher income    Agree   0.112561  0.000475  0.111633  0.113495\n",
      "      Higher income Disagree   0.044076  0.000305  0.043482  0.044678\n",
      "      Higher income  Neutral   0.019958  0.000208  0.019554  0.020371\n",
      "Intermediate income    Agree   0.347272  0.000715  0.345872  0.348674\n",
      "Intermediate income Disagree   0.089207  0.000434  0.088360  0.090060\n",
      "Intermediate income  Neutral   0.058725  0.000359  0.058025  0.059433\n",
      "       Lower income    Agree   0.241527  0.000653  0.240250  0.242808\n",
      "       Lower income Disagree   0.052818  0.000341  0.052155  0.053490\n",
      "       Lower income  Neutral   0.033857  0.000284  0.033306  0.034417\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(4): 3708.4454 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(4): 3555.9931 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6a9825f9-9ef2-4cd1-950b-9493979e8680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "         employment  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0          Employed     327071.0  71.656313   10.831223    17.512464\n",
      "1  Not in workforce     208858.0  72.412014   11.057241    16.530745\n",
      "2        Unemployed      50100.0  72.318849   10.121914    17.559237\n",
      "\n",
      "Cross-tabulation of employment and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 585757\n",
      " Number of observations: 585757\n",
      " Degrees of freedom: 585756.00\n",
      "\n",
      "       employment     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "        Employed    Agree   0.384153  0.000696  0.382790  0.385517\n",
      "        Employed Disagree   0.114305  0.000453  0.113419  0.115196\n",
      "        Employed  Neutral   0.064605  0.000354  0.063914  0.065304\n",
      "Not in workforce    Agree   0.250384  0.000614  0.249182  0.251590\n",
      "Not in workforce Disagree   0.059173  0.000342  0.058507  0.059847\n",
      "Not in workforce  Neutral   0.039442  0.000286  0.038885  0.040007\n",
      "      Unemployed    Agree   0.064564  0.000371  0.063840  0.065295\n",
      "      Unemployed Disagree   0.013946  0.000170  0.013616  0.014283\n",
      "      Unemployed  Neutral   0.009428  0.000142  0.009154  0.009711\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(4): 1349.9210 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(4): 1361.9806 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('employment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0c610dd9-edd5-4b18-802b-bc1052e1f6b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "  ideology  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0     Left     391698.0  72.061823   10.283336    17.654840\n",
      "1    Right     194331.0  71.540982   12.614621    15.844397\n",
      "\n",
      "Cross-tabulation of ideology and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 585757\n",
      " Number of observations: 585757\n",
      " Degrees of freedom: 585756.00\n",
      "\n",
      " ideology     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "    Left    Agree   0.465386  0.000714  0.463987  0.466786\n",
      "    Left Disagree   0.128137  0.000478  0.127204  0.129076\n",
      "    Left  Neutral   0.074815  0.000385  0.074064  0.075573\n",
      "   Right    Agree   0.233714  0.000609  0.232523  0.234909\n",
      "   Right Disagree   0.059286  0.000341  0.058621  0.059959\n",
      "   Right  Neutral   0.038661  0.000277  0.038123  0.039207\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(2): 153.9313 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(2): 154.7410 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('ideology')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bd251333-fa60-49ca-b47b-27011151e655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "                                 placeid  group_count   pc_Agree  pc_Neutral  \\\n",
      "0                          Close nowhere       3525.0  66.154107   14.308190   \n",
      "1                  Closest international       8076.0  67.827070   12.052413   \n",
      "2                       Closest national      10946.0  72.728537    8.651397   \n",
      "3     Closest national and international       5392.0  68.257285   10.477126   \n",
      "4                   Closest sub-national      29777.0  71.617412   11.620125   \n",
      "5      Closest sub-national and national      42634.0  74.343001   10.534150   \n",
      "6  Closest subnational and international       4173.0  59.476506   17.012802   \n",
      "7                      Equally close all     481506.0  72.376056   10.221531   \n",
      "\n",
      "   pc_Disagree  \n",
      "0    19.537703  \n",
      "1    20.120517  \n",
      "2    18.620066  \n",
      "3    21.265590  \n",
      "4    16.762462  \n",
      "5    15.122849  \n",
      "6    23.510692  \n",
      "7    17.402413  \n",
      "\n",
      "Cross-tabulation of placeid and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 585757\n",
      " Number of observations: 585757\n",
      " Degrees of freedom: 585756.00\n",
      "\n",
      "                               placeid     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "                        Close nowhere    Agree   0.003860  0.000099  0.003671  0.004060\n",
      "                        Close nowhere Disagree   0.001342  0.000059  0.001232  0.001463\n",
      "                        Close nowhere  Neutral   0.001006  0.000049  0.000916  0.001106\n",
      "                Closest international    Agree   0.008118  0.000137  0.007854  0.008391\n",
      "                Closest international Disagree   0.003560  0.000092  0.003385  0.003744\n",
      "                Closest international  Neutral   0.002143  0.000072  0.002006  0.002289\n",
      "   Closest national and international    Agree   0.004950  0.000109  0.004741  0.005167\n",
      "   Closest national and international Disagree   0.002854  0.000082  0.002699  0.003019\n",
      "   Closest national and international  Neutral   0.001461  0.000060  0.001349  0.001583\n",
      "                     Closest national    Agree   0.011704  0.000166  0.011383  0.012033\n",
      "                     Closest national Disagree   0.004048  0.000095  0.003866  0.004238\n",
      "                     Closest national  Neutral   0.002838  0.000079  0.002687  0.002998\n",
      "    Closest sub-national and national    Agree   0.052856  0.000333  0.052208  0.053512\n",
      "    Closest sub-national and national Disagree   0.011033  0.000157  0.010729  0.011346\n",
      "    Closest sub-national and national  Neutral   0.009171  0.000142  0.008898  0.009453\n",
      "                 Closest sub-national    Agree   0.035446  0.000271  0.034917  0.035982\n",
      "                 Closest sub-national Disagree   0.008614  0.000135  0.008354  0.008881\n",
      "                 Closest sub-national  Neutral   0.006765  0.000127  0.006520  0.007019\n",
      "Closest subnational and international    Agree   0.003891  0.000097  0.003706  0.004085\n",
      "Closest subnational and international Disagree   0.002017  0.000066  0.001891  0.002151\n",
      "Closest subnational and international  Neutral   0.001184  0.000051  0.001087  0.001289\n",
      "                    Equally close all    Agree   0.578275  0.000711  0.576881  0.579668\n",
      "                    Equally close all Disagree   0.153955  0.000513  0.152952  0.154963\n",
      "                    Equally close all  Neutral   0.088908  0.000409  0.088110  0.089712\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(14): 2732.4124 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(14): 2608.0715 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('placeid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c2f95a39-d47e-4b29-8097-15800fa151cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "    impact  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0      Bad      40542.0  81.168159    7.182317    11.649524\n",
      "1  Neutral      60867.0  70.103631   13.266691    16.629678\n",
      "2     Good      41693.0  68.310074   10.426720    21.263206\n",
      "\n",
      "Cross-tabulation of impact and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 143102\n",
      " Number of observations: 143102\n",
      " Degrees of freedom: 143101.00\n",
      "\n",
      "  impact     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "    Bad    Agree   0.231521  0.001248  0.229083  0.233977\n",
      "    Bad Disagree   0.030457  0.000506  0.029480  0.031464\n",
      "    Bad  Neutral   0.023496  0.000459  0.022614  0.024413\n",
      "   Good    Agree   0.158485  0.001108  0.156325  0.160669\n",
      "   Good Disagree   0.088746  0.000866  0.087064  0.090458\n",
      "   Good  Neutral   0.042686  0.000619  0.041489  0.043914\n",
      "Neutral    Agree   0.282550  0.001357  0.279898  0.285216\n",
      "Neutral Disagree   0.072290  0.000776  0.070784  0.073826\n",
      "Neutral  Neutral   0.069769  0.000770  0.068275  0.071294\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(4): 7933.4455 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(4): 7907.6815 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('impact')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f2d323a9-c5bf-4b68-a318-8223bef43a62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "         trust_foreigners  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0  Don't trust foreigners     155590.0  76.729929    9.036168    14.233903\n",
      "1        Trust foreigners     123643.0  62.088565   14.119321    23.792114\n",
      "\n",
      "Cross-tabulation of trust_foreigners and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 279233\n",
      " Number of observations: 279233\n",
      " Degrees of freedom: 279232.00\n",
      "\n",
      "       trust_foreigners     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "Don't trust foreigners    Agree   0.426965  0.001043  0.424922  0.429011\n",
      "Don't trust foreigners Disagree   0.068245  0.000524  0.067226  0.069278\n",
      "Don't trust foreigners  Neutral   0.065985  0.000525  0.064964  0.067022\n",
      "      Trust foreigners    Agree   0.255465  0.000927  0.253653  0.257285\n",
      "      Trust foreigners Disagree   0.110875  0.000674  0.109560  0.112203\n",
      "      Trust foreigners  Neutral   0.072465  0.000554  0.071387  0.073558\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(2): 10933.5434 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(2): 10916.6513 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('trust_foreigners')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ae1df186-0072-4452-9469-0d7546d39187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "        foreignneighborsOK  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0      Foreign neighbor OK     447975.0  69.496396   11.693855    18.809749\n",
      "1  Foreign neighbor not OK     118807.0  80.863727    7.811433    11.324840\n",
      "\n",
      "Cross-tabulation of foreignneighborsOK and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 566538\n",
      " Number of observations: 566538\n",
      " Degrees of freedom: 566537.00\n",
      "\n",
      "      foreignneighborsOK     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "    Foreign neighbor OK    Agree   0.526238  0.000727  0.524814  0.527663\n",
      "    Foreign neighbor OK Disagree   0.169082  0.000546  0.168015  0.170155\n",
      "    Foreign neighbor OK  Neutral   0.093871  0.000431  0.093029  0.094719\n",
      "Foreign neighbor not OK    Agree   0.171251  0.000553  0.170170  0.172337\n",
      "Foreign neighbor not OK Disagree   0.020773  0.000209  0.020368  0.021186\n",
      "Foreign neighbor not OK  Neutral   0.018786  0.000198  0.018402  0.019177\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(2): 10253.9117 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(2): 11265.3411 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('foreignneighborsOK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cea37cbf-7a5a-49b3-a1a5-a6a7104d897d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FULL SAMPLE RESULTS\n",
      "                  ntnlpride  group_count   pc_Agree  pc_Neutral  pc_Disagree\n",
      "0  Not proud of nationality      64178.0  60.842236   15.585290    23.572475\n",
      "1      Proud of nationality     493820.0  73.603490   10.218593    16.177918\n",
      "\n",
      "Cross-tabulation of ntnlpride and jobs\n",
      " Number of strata: 1\n",
      " Number of PSUs: 557756\n",
      " Number of observations: 557756\n",
      " Degrees of freedom: 557755.00\n",
      "\n",
      "                ntnlpride     jobs  point_est  stderror  lower_ci  upper_ci\n",
      "Not proud of nationality    Agree   0.072560  0.000379  0.071820  0.073307\n",
      "Not proud of nationality Disagree   0.027440  0.000237  0.026980  0.027909\n",
      "Not proud of nationality  Neutral   0.014815  0.000178  0.014470  0.015168\n",
      "    Proud of nationality    Agree   0.635377  0.000708  0.633988  0.636763\n",
      "    Proud of nationality Disagree   0.152492  0.000529  0.151457  0.153532\n",
      "    Proud of nationality  Neutral   0.097316  0.000441  0.096456  0.098183\n",
      "\n",
      "Pearson (with Rao-Scott adjustment):\n",
      "\tUnadjusted - chi2(2): 2176.6921 with p-value of 0.0000\n",
      "\tAdjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n",
      "  Likelihood ratio (with Rao-Scott adjustment):\n",
      "\t Unadjusted - chi2(2): 2073.8022 with p-value of 0.0000\n",
      "\t Adjusted - F(0.00, 0.00): 0.0000  with p-value of nan\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    makeappendtable('ntnlpride')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67729233-61bf-4781-bd7a-3b13a516ffc2",
   "metadata": {},
   "source": [
    "#### Theories on migrant views on job preferences: Figures 4.1 - 4.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c7b3f106-9745-43b4-abe1-1a5c411fb81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medians by group\n",
      "Locally born national\n",
      "73.94892492765187\n",
      "Naturalized\n",
      "64.81874689233791\n",
      "Non-national\n",
      "43.609152592945435\n",
      "Average differences between groups\n",
      "Locally born national cf Naturalized\n",
      "11.36172999879211\n",
      "Naturalized cf Non-national\n",
      "7.946735576414203\n",
      "Locally born national cf non-national\n",
      "19.443426123730074\n"
     ]
    }
   ],
   "source": [
    "#Figure 4.1\n",
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    ntrlzd=makescattertable('ntrlzd')\n",
    "ntrlzd=ntrlzd.loc[((ntrlzd['group_count_Non-national']>0)&(ntrlzd['group_count_Naturalized'])>0)].copy()\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "plt.scatter(ntrlzd['pc_Agree_Locally-born national'],ntrlzd['pc_Agree_Non-national'],color='k')\n",
    "plt.scatter(ntrlzd['pc_Agree_Locally-born national'],ntrlzd['pc_Agree_Naturalized'],color='lightgray')\n",
    "plt.xlabel('Support among nationals from birth')\n",
    "plt.ylabel('Support among nonnationals/naturalized respondents')\n",
    "plt.show()\n",
    "\n",
    "print('Medians by group')\n",
    "print('Locally born national')\n",
    "print(ntrlzd['pc_Agree_Locally-born national'].median())\n",
    "print('Naturalized')\n",
    "print(ntrlzd['pc_Agree_Naturalized'].median())\n",
    "print('Non-national')\n",
    "print(ntrlzd['pc_Agree_Non-national'].median())\n",
    "print('Average differences between groups')\n",
    "print('Locally born national cf Naturalized')\n",
    "print((ntrlzd['pc_Agree_Locally-born national']-ntrlzd['pc_Agree_Naturalized']).mean())\n",
    "print('Naturalized cf Non-national')\n",
    "print((ntrlzd['pc_Agree_Naturalized']-ntrlzd['pc_Agree_Non-national']).mean())\n",
    "print('Locally born national cf non-national')\n",
    "print((ntrlzd['pc_Agree_Locally-born national']-ntrlzd['pc_Agree_Non-national']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6ab7dca7-f1f9-4383-a262-8127d9162c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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36s2VLjRTY3ztlw9ullKqjVIhBheIvk99fb39PD5cc64X8njCoNghLoLJ9aNhzpw55tRTTzVNTU1mr732stteeOEF8/DDD5vbbrstH8cIAAAAAL7HJMH8UEik+zDVVFBNm8xXz6tMl1LqXDlRI/98ymawANKHuKkq0vIZ4iKY+mzM4i0UBWnXX3+9efvtt+31HXbYwfZZc4K2MI9RBQAAAIBMJwk6tCSPcM374FIhh0K1fN636rFWWVlpli1blrQCUUFWa2tr0arDdIxuBgsgOX6X4TYnyipYCxuCNQAAAADZ0ilVc3Nz2iqXqqoqJgl6cF87DeZVWaTKoULcp5oKOmPGjO5jcDjfW1VjVIeFR7FCXAQzJ8ppYfC6devMV1991WMbwRQAAACAKGGSYOEoyCrGfciSy2hRoKJsoxghLoLHdbC2du1ac+GFF5p7773XrFq1KmEJKgAAAABEBZMEo0HhWU1NDUsuI6JYIS4iEKxdcMEFZtGiRebmm282J554ornxxhvtWvP/+Z//MXPnzs3PUQIAAACATzFJMDoUolVXVxf7MAD4iOtn9gcffNDcdddd9snkhz/8oU3ot9tuOzNmzBjz+9//3nzve9/Lz5ECAAAAgA8xSRAAoquv23+wevVqs80229jPteZY12W//fYzTz31lPdHCAAAAAAJqIl8Z2enaW9vt5fFmsumJWNaFpiKmp47/Zn8ctwAgCJUrClUa2lpMVtvvbUZP3687bW255572kq20tJSDw4JAAAAANxP7VPVmAKuYkztc75nukmCfjtuAECBgzUt/3zttdfMAQccYGbPnm2OOuooc8MNN9gpN/PmzcvxcAAAAAAgNYVTS5cu7bVdYZWzvVjhWqpJgn49bgBA9vpszLHuuLW11SxevNj2Wdt1111NEHV0dNg/YPpDpz+EAAAAAPxJpy/Nzc1p+5lVVVV1B1p+ENTjBtw8xpOFykCYc6Kcx9JUVlbaDwAAAADIN+fEPRWtptF+JSUlxi+CetxAJljijChzPbxAnnzySbsEVFVq+jj66KPN008/7f3RAQAAAECMdOGU2/0KJajHDaTjLHGOf+w6S5x1OxBmroO13/3ud+aggw4ym222mTn77LPtx6BBg8y0adPM/Pnz83OUAAAAAPD/V8F4uV+h+Pm4169fb5qamsyCBQvspa4DmS7/VKVaKm1tbUy+Rai57rG2ww47mNNOO82cc845PbZrcMFtt91m3n77bRM09FgDAAAAgiGovcr8etyNjY1m1qxZ5sMPP+zettVWW5n6+nozffr0gh0Hgqmzs9P2XU9H7aOitsSZnnPB5iYncl2x9v7779tloPG0HLSlpcXtlwMAAACAjOnEVH2bUikrK/PdCawfj1uh2owZM3qEarJs2TK7XbcDqbDEOTGFMQrSFTrq90uXus6y2HByHaxVVFSYhQsX9tr++OOP29sAAAAAIJ9URaBzj/hlk6r40na/Nkv303Fruacq1RItYHK21dbWsiwUgV3iXCz0nIse14/u8847z/ZVe/XVV83kyZPttmeffdbceeedtlwYAAAAAPJNIZSW5wRtqZVfjlvD5+Ir1eLDNYUA2q+6urqgx4bg0GNXj+F0S5y1XxRk2nNOzwF+f65CHoO1008/3ZYoX3fddebee+/t7rt2zz33mJqaGrdfDgAAAACyohPTIPZt8sNxpzv5d7sfwkfVigpW9RjQMuYpU6aYfv36JVzirBA2SEuz88UJzFPp6uqy+xX7OQBFCtb0APnFL35hTj75ZPPMM894eBgAAAAAgEJJ1+/N7X4IFzdDLZwlzArgYkMlVaopVPPr0ux8oOdcNLmeCqpU9c0337RTPcKCqaAAAAAAolaNpHM6DSpIdEqoCiMFKRpQF1+lhMJVhBWDM9Qi/nHhVJ01NDQknBjLFEympIZJXqeCTps2zTz55JOmUE80l1xyiRk7dqzZdNNNzbbbbmuuuOKKHr/g+vzSSy+1T0Ta56CDDjLvvvtuQY4PAAAAAIJIAY7TIzs+/HCu19XV+SLoCSOFVwpXpk6dambOnGkvdb3Yk1hzGWrhLHEuLS21l1EL1WJ7zqUSpZ5zUeE6WDvssMPM7Nmzzfnnn28WLFhgHnjggR4fXrrmmmvMzTffbG644Qbz9ttv2+vXXnut+fWvf929j65ff/315pZbbjEvvPCCfYAecsghZt26dZ4eCwAAAACEiaqOVH1UXl7eY7sq1ZJVJcG7irD44RGqHtT2YoZrboZaoDen55zupw0bNvS4Tde1PUo956LC9VLQvn2TZ3F6cHg5jvnII480W2yxhbn99tu7tx133HG2Mu13v/udfVCOHj3aTipV0Ccq09O/0ZTS7373uxl9H5aCAgAAAIiqfCxJZFlg6iW4ycKrYi/BVfGMKujSmT9/vjnhhBMCsbS10BSMqkDooosusiGaQ/eLCoM0EJLQ2v/c5ESup4LGp675NHnyZHPrrbead955x1RVVZnXXnvNDk2YN2+evV1PNhpVq+WfDv3H99prL/Pcc88lDda+/PJL+xF7hwEAAABAFCn8qK6u9uzr6UQ0vpG9wjWFLVFqZJ9rRZiXP5NMZTqsYtSoUaapqak7RFuxYoU599xzMxp2EGbOUlrdD0888YSZOHGiGTlypL1/Fi9ebH++zc3NpqamJpKhY1i5DtYKSUtOFXqNHz/ePuj0IL3qqqvM9773PXu7QjVRhVosXXduS+Tqq682c+bMyfPRAwAAAEC0KFRTMBRPIZuzPcrhmoIoL/fzmqrMFIilGmoxfPhw84Mf/CBlQBi7tDVKy4pjg1MVJb300ku99ilmcAqf9FiThQsX2mWaGiagD33++OOPe35w9957r/n9739vy0yV7v7v//6v+eUvf2kvc/HTn/7UPuE7H4me+AEAAAAAmVMQky4QUgGEy25EoZJpRVim+xV6qIV+dqtWrUobqmUy7CCM/B6cwifB2k033WQOPfRQM2TIEFviqA+tNz388MPNjTfe6OnBXXDBBbZqTUs6d9llF3PiiSeac845x1acibNe+ZNPPunx73Q9di1zvE022cQec+wHAAAAAESdAhAt8VOvLV26CUScnmqpdHV12f2iyqkIS9ZvTtsrKirsfn4baqHrI0aM6NF/fdKkSXbAoS4T9WOP2rADvwen8MlS0F/84hfmV7/6lTnzzDO7t5199tlm3333tbedccYZnh3c2rVre/1yKkF3+ryNHTvWBmiqoNt9993tNi0d1XRQNQQEAAAAAGTedN3pD5VNn6x0oZrb/cLIqQjTEkmnAszhhG11dXVF77+ln7f6gMUOI1DI6vQ3nzZtmi2CiS1oUTXi3Llz7fl5VCu0MllKq9uLGZzCBxVr7e3ttmIt3sEHH2yXVXrpqKOOsj3V/vSnP5nW1lZz33332cEFxx57bPeDUmWlV155pXnggQfMG2+8Yb7//e/bSaHHHHOMp8cCAAAAAGEO1RT2xC/xc/pk6fZ0NKAgE5nuF1bJKsIUuPipH5kz1ELTP3W5fPny7lBN5+UaYBBL17Vdt0e1QivdUlq/BKfwVp+NLhe4a/TuhAkT7DLNWOp9psZ8d999t2cH9/nnn5tLLrnEBmr6JVZgpl/qSy+91AwcONDuo8O/7LLL7PRQhX777befXa6qKaL5GKMKAAAAAGGiSqTKysqkfbOcKpuWlpaUgYAz8TBVRdqAAQPsuVqypZBRu99jK8JUxeTnwEVLgxWaPfLIIzZES7T0U6vL1JpJxTj6PNPHThSqP7XEV6GaX4JTeJcTZRSsXX/99T2+uEI0Lf3cZ5997Lbnn3/ePPvss+a8884zP/vZz0zQEKwBAAAAiCoFJlOnTk2736JFi9JOMkw3HE7hQpSnggY9CNSqsmuvvTbtvj/84Q/Nyy+/bD/3UxVeIQUtOEX2OVFGNbjqqRZr2LBh5u9//7v9cJSWlpo77rgjkMEaAAAAAESVl5MMndBM+8ZWrqlSTf24CNWCS6GQhglmYuTIkbZSLcoVWs5SWoRfRsGayjYBAAAAAOHj9SRDhWeq8HCmhKqn2uDBg1n+GYJqK61aU//zdC6++GKz9957U6GFSIh210gAAAAAiLh8TDLUvykpKfH4SFHs6bAaJKigNF0fvcmTJxOkIjKyCtb0C6YpnEuWLDFfffVVj9s0BQQAAAAAEKxJhpr+qTAkNlxjkmG0+m8502HjA1ZnOqz6pWmAQao+elryS6iGKHE9FXThwoXm6KOPNttss435xz/+YXbeeWdbCqovM3HiRPPEE0+YoGF4AQAAAICoY5Jh8SvCink/u5kO29nZSR89hFqH11NBY+25557msMMOM3PmzDFDhgwxr732mh21+73vfc+O1D399NNN0BCsAQAAIMr8Wj0TFX66//10LFGqCHMqvIo5QdPtdFj9H+ijh7DyfCporLffftssWLDgX/+4f3/zxRdf2LXzl19+uampqQlksAYAAABElV+rZ8IsNvR59913za233mqX2vnh/g/DJEO/Pqb1c9dxJapt0TaFUrW1tfa8uhghoNvpsPTRA/6lr3FJKbTTV03J/3vvvdd928qVK91+OQAAAABFrp6JX/rl9FPS7fCW7lMtt1Nl0MyZM81ll13WI1QT7v9wPqYVpiZbZumEa+pdpv3CMB0WiArXwZpG5j7zzDP288MPP9ycd9555qqrrjInn3yyvQ0AAACA/6WrnhFVz2g/5Df0icf9H87HtNuKsGJNh022nFPb1XPPzXRYJKfHoZbfakWgLvldj1Cwpqmfe+21l/1cfdY0EeSee+6x77rcfvvt+ThGAAAAABGrnolS6JMI93/4HtN+rwhzpsNKfLjGdNj8Vq7qUtepUo1IsKZpoLvuumv3stBbbrnFvP766+aPf/yjGTNmTD6OEQAAAEDEqmeiFvokw/0fnsd0ECrC1INOAxTKy8t7bNdxF3OwQpj4ebkyChSsAQAAAAg+v1fPhE22YQ73f3ge00GpCFN41traaqd/zp8/3162tLQQqkVguTKy02djprXIIeZmjCoAAAAQBjpx09IjVUkkOiXQib6qVHRCXewT/TBQDyUt98oU9394H9OJppaqUk2hWr7CK90fa9asMV9//bXp37+/XX2WrHIOxX8eUJgZ9Om8UcqJ+hfsqAAAAAD4hlM9o6VHOsGODSL8VD0TFs4ywGShT6xc7n+FS1p2qgo5VWbp+0blZ1iIx7TbgCrRz0PhWU1NTcF+TgoG9H10zA4du76vgoNkCOOit1wZ2SFYAwAAACLK6acUXz2jACif1TNRlCr0iTds2DD7M1H4kmsllH6W+r5R+Vnm8zHtNqBK9/MoREWSjlkDG+Lp/+BsT3Ts2YZxCPZyZWSHpaAsBQUAAEDERbnKqdCShS377ruveeyxx8zq1auzCsWchujxp3dOhVHUGs97/ZhOFlDFLuWMDZxy/Xl4US2mr9Hc3NwjHIs3YMAAU1VV1eNru/2/InzLlWFc5USugzU9EO68806zcOFCs3z5crNhw4Yetz/xxBMmaAjWAAAAgGAIw/K0+NBnxYoV5vjjj886hHFO1pNNHS3UyXpYA1q3AVWuPw+vqsU6OzvtEIJ0dKwlJSU5hXHInBO6SqLlylELwSPZY03vrihYO+KII8zOO+/MLxMAAACAggjL8jSFKc4yQCeESTYlUOdbmhKoZaHJQiqFWclCHOfrqAJJ++Vr+WGYl6E6QW4qXV1ddj8FVLn8PLJduplIumNOtJ/b/yvcYwl++LgO1u6++25z7733msMPPzw/RwQAAAAAcbwMHPzEi1Cs2A3Rky171HI3bQ96BY7bgCrbn4fuv3T/tq2tzVbPZFLgotA5E7H7ZRPGwb1CD7CAz4K1gQMHmu222y4/RwMAAAAAcbwOHPzEi1CsmA3RVXGnyptcKu78zm1Ale3Pw+tqMS2T1jGlW9ap/XIJ45B75SqCra/bf3DeeefZcl5mHgAAAAAoBDeBQ9B4EYqp0kXLyJKFitquhvPar5gVd0HlBFSpxAZU2f48vK4W0/dJ9/gqKyvrcZxu/68AsqhYe+aZZ8yiRYvMX/7yF7PTTjvZX6r4MmAAAAAA8EqYl6c5IUy6KYGpQjFVvqj4QcsutX+ihujq3ZSPirFiL0MtBCegSrQU2bmvR40a1X1fZ/vzyEe1mLM8Or43oc7jFarFL59O9X9NFsYBUee6Yq20tNQce+yx5oADDjDf+MY37C9i7AcAAAAAeCnMy9OcEEbiwwo3oZjTEL28vLzHdoVy+exxVsxlqIWkc11VmcU/xrQE+ZxzzjG77rprjyKTbH4e+aoW07GPGzfODsnQ99elpnomO39P9n/V99Z2zvuBnvpsdLGmUwn3/PnzzcEHH2xT6iiOUQUAAABQWDplaW5uTtsrSmFBUCtpEk3VVIjhdkqgep7lqyF6oq8tCmrSVdy1tLQEtsda/M9p7ty5tshkxYoVZvHixWbDhg3dj7v40MztzyPZkA5HIYMt/TydZdgK2RToBfX3C8hnTuQqWJPNNtvMvP3222bMmDEmLAjWAAAAAH/zU+CQL/kMxfIR/Ckwc6rttOxREi17DPpU0Nifj0LEZD3lvAoR9VjPdOkmgAAGa5paoakuxxxzjAkLgjUAAADA/wgciheqKTiLP3WMDc7Ei4o7P1dbNTU1malTp6bdTz3Jc532SLUYEJycyHUTgp/85Cd2MqieMPfYY49e67u1thwAAABAcATlJF4nOTrBCcKxhuXnpiotBWaJ6jG0Td9PhReq0qqpqfFFxV2iAFb3j44plwC2kIMadL+WlJTk/HUA5J/rYO273/2uvTz77LO7tzmTTnSpJ14AAAAAwZCvECJfCBwK+3NTUJZs6aPoPFBLdLWfqrRyrdTK15Jh3U/O9mzvn6gMagCQ52BN70QAAAAACL58hhAIx8+tkFVauVLIl+44NMXTWdblttpPFXjqoZZuUIMz1AFANLgO1sI0tAAAAACIKjchRFSXWvpRoX9uQarScoKyVLq6uszy5cvNp59+6rraT8taNaxB/eacVVsO575WTzm/DJwAUBh9s/lH7733njnrrLPMQQcdZD+0LFTbAAAAAARDpiGE9kN0f25OlVaykE7bNaTAD1Va6e4Xx4oVK3rt61T7qRowFQ1i0LCG8vLyHtt1H4Vl+imAPAdrjzzyiNlxxx3Niy++aAcV6OOFF14wO+20k3nsscfcfjkAAAAAPg4hMt0P4fy5OVVaEh+u+a1KS1VnuVK1X6JlnrEUnrW2ttrpn/Pnz7eXapmUa6imfuWaPLpgwQJ7Sf9yIBj6bEz3rBFnwoQJ5pBDDjFz587tsX327Nnm0UcfNYsXLzZhHqMKAAAAhEFnZ6cNB9KprKxkWEBIf24KbjKd4tnY2Ging8YOMlClmkI1v1Rp6dS2ubk551CxGI/5RPevquAUavrl/gWipMNFTuQ6WBs0aJB54403zPbbb99j+zvvvGOr19atW2eChmANAAAAUZNJCDFgwABTVVVFj7UQ/tyyCXLcBHF+G+zghu6H0tJSUyj6WahvW/ypufPzY4kp4O+cyPVS0JEjR5pXX32113ZtGzVqlNsvBwAAAKAIdNKeruF8WVkZoVoIf25OkBMbqommXWq7bk9EIVp1dbU54YQT7GWqUK1Yyxp1IqxKuvhloQobdS5bqCWlmdL9ooBToVrfvn3NpEmTzGGHHWYvnQEJP/7xj81XX31VsGMC4I7rZ4wf/ehH5rTTTjPvv/++mTx5st327LPPmmuuucace+65br8cAAAAgCJxJiCqAim2AkohhMKZVBMSEcyfW2yQE0/bFObU1taampqarKvRir2sUf9/VZg4gx4UlA0ePNjeFj8NNJ7uQ2ffQlAFoO6nadOm2fZK+vnF9ntTC6aFCxfa+++WW26hcg3wIddLQbW71tFfd9115qOPPrLbRo8ebS644AI7HTSI72ixFBQAAABRptf48SFEEF/XR002PzdVj02dOjXt11ZDflWlhW1ZY7qloqp2K2SgrIq+22+/3cybN89eV9WaY8OGDfZSBSwK13QfFvv+A6KiI5891mJ9/vnn9nLIkCEmyAjWAAAAAESBgpyZM2em3U/TLrXk0w1Vw6nxf/wSU4eCIVVeaYJmMXuz6byv2FWaTij6+uuv2+NQT7fYUC02XPvkk0/MoYceav+NH+4/IAo6XOREWS8eX758uW2aKePHj894vToAAAAAoDjS9Wdzu1+iZY3JKBhStZj2y6YaLt9LRQtVpRkb7KVtit63r/1ZTJw40bz00ku+uP8A5BisqUrtJz/5iX2nwylNVVp+/PHHmxtvvJE+DAAAAABcC8LEyTDQ/aqqJw0qSLR4yakq035u6Wfn5X75pP9nSUlJYKaWxhayJLr/WM4NFI/rqaCnnnqqeeGFF8yf/vQn097ebj8eeughm57/53/+Z36OEgAAAEBoqS+XlhCq95eWKepS15NNp0T2FFZqiIDEBy/OdfXUzibUzGc1XBgo/Mo2VFyxYkXS+09hnVaTtba22opBXeq6tgPIP9c91pR8P/LII2a//fbrsV3vLmndt1LyoKHHGgAAAFAcfm92H1aJJneqcb9CtWzvb6fHWrpquKj2COvs7LShlxvpeqz5bRgDEBZuciLXFWsjRoxI+IupbcOGDXP75QAAAABElIIYhTuJQhhnW21trd0P3lJ4ppBH0z81qECXCmxyCTHzWQ0XBrHDEjLhtF665pprun8fYu+/TCrg2traEv5+AfCO62DtZz/7mR33q19Qhz6/4IILzCWXXOLhoQEAAAAIMzfN7uE9BTRqgq/pn7r0IvBSMKcqw/Ly8h7bVWkV9epD9T5zQ5VqOvdeuHBhwvvP6amWSldXVyBXlQFBktFv9oQJE3q84/Duu++arbfe2n7IkiVLzCabbGLXfdNnDQAAAEDYmt0jcwp/ampqGEaRoK2SwrVUYZjuo7KyMnu5cuVKc8opp9jilkT3X6YVcG4r5QDkIVg75phjXH5ZAAAAAEiNZvfhr4bDv6lYRY/lVD3RRo8e3d16Kd39l2kFnNtKOQB5Gl7w/vvvm2222caEEcMLAAAAgMKj2T2iSOedquSLrSQbMGCArVRzM2hAvzOa/pmqIk1ft6qqqlfPu0L9flO1iKDKy/CCXXfd1ey8887mv/7rv8yLL77oxXECAAAAiDCa3SOKdLI+btw4GyorONalwi+30zudCrhUVq1aZe6++27T1NRU0CEgmjqr/9fUqVPNzJkz7aWuazsQNhkHa1rfffXVV5vly5ebo48+2v4C/+hHPzIPPvigWbduXX6PEgAAAEAo0eweUaRQrKSkxJSWltrLbCvKFMZVVFT0Wu6poQVXXHGF2XfffVMGW6p66+zsNO3t7fbSiwmi+h4zZszoNZhElanaTriGyC4FjaV/8txzz5kHHnjAfmh4wUEHHWQDt6OOOsqMHDnSBAlLQQEAAIDiYtlYsPHzKx4FVeecc44ZNWqUPRfXUMHFixebDRs29NjPCe+cwDrRklQFdPr5ua2ei1/enWzaL8u7EcacKKtgLZ6mhCpgu//++80LL7xg5s2bZ8444wwTFARrAAAAAJB9sDNr1qweYYrCEy3zpeIwv5zqsExP651g67XXXrMVZMmoCi6bcE1LTlUdl86iRYsYboHo9VhLZfvttzfnnXeeeeqpp8xHH31kDj74YC++LAAAAADAx1j2VzyqDlOg6aZWRvvqZ6NVZ6m0tbVltSxUFXBe7gcEgeu5u19++aWtSvvggw/M2rVrbanphAkTzNixY+3tI0aMsB8AAAAAkCmdxK9Zs8YuS9NytMGDBxdlkiG8CXa0TT+/2tpaU1NTw7K/PNDS22RLLlOZOHFi2p+HerTp91H939xIN0zB7X5AqIK1Z5991pbyaliBfslUErfpppua1atX27Btm222scMMTj/9dDNkyJD8HjUAAACA0ARV+ej1hOIHO3oMLl261O7Hsj/vZVv1lWlP9Njfx0ypt56WmqoqLlHg6ixF1X5eP5cF6TkPEQzWNJRAzQ81TeTRRx81kyZNsqGa4/3337dPlgsWLDC/+tWvzF133WW+9a1v5fO4AQAAAIQgqNKxKnyJp2N3tvvtmPEvLPsrrmyqvhQ0ZRo2xU8azYQq4VSQo2XA+j6x4Zrzfevq6jKuYMz0uSxIz3kIn4x+U4444gjzxz/+0QwYMCDh7apW08dJJ51k/v73v/PECQAAABRJkIIqnXSnO3dQryc1jg5r5UmQp2my7K+40lWHxXN+h0455RQbOqWqSNO5vyq+sqGBFZo8mmighUK1TAdaZPpcFqTnPISTJ1NBg46poAAAAAgDvbRvbm5Oe8JcVVXli6Cqs7PTtLa2pt2vsrLSda+nIAj6NE2FgvrZpFv219LSEpiwMKjDIyTdqb0mfTrBVrIwKnbfXMOoXELjTJ/LNEjxnXfeCcxzHoLDTU7kurZTv3zOE6S8+OKLZv78+WbHHXc0p512WvZHDQAAACAnTn+hfDQlz4dMezhl0+spKIFIfBjiTNNUxY/bcK3Q1W9eL/vziyBVESarDlMwdt1119l+aon+H05oFr98UiFUWVlZTqFabK8ztZHKptdZps9lq1atCtRzHsLJdcWafhkVoJ144om2LHvcuHFmp512Mu+++64566yzzKWXXmqChoo1AAAAhEF7e3tGUwL1JnlpaamJUsWanxqbO5VeyX5W2VR6FbP6LdH3jq2OCpKgVhFmGwZ6/XvhVa+zTJ/Lhg8fbgcqBuU5D+HMiVwHa8OGDTPPP/+8DdSuv/56c88999iJoRpq8OMf/9gOMggagjUAAACEQdCWVhZq6arfGps3NTWZqVOnpt1v0aJFGU3TTFb95txn2VS/hbnKy8/3Y5B5ubw00+eyLbbYwnzyySeBec5DcOR1KajKKDfZZBP7+eOPP24nhsr48eMZWgAAAAAUkapN8tmU3GsKLBTCpDoZ17K0XEM1vzU2z2WaZnyANXnyZFth5YRBffv2NRMnTrRLAFesWGFeeeUVU1tba2pqavK+LDSTENCvdL/G3o+xtE2PwULcj0Hl9SCSTJ/LRowYkXY5qJ+e8xBOroM1Lfu85ZZb7KTQxx57zFxxxRV2+0cffWQf1AAAAADCG1R5Ld+9nvw4dTTbaZqJlik6AZpMmzbNzJ49295vsf+/uXPn2jAuyMFXvun+SbX0UI8l/V5xPxamv2Omz2UKkoP2nIfwcR2sXXPNNebYY481//3f/21OOukks9tuu9ntDzzwgNlzzz3zcYwAAAAAfBBU5YuOSeGW1z3Q/DrMQcsk1fMp3TRN7ZdumWJsqDZv3rxeX2vUqFF2e6rgoVD8vFw0lypC5GcQSabPZUF8zkPEgzWl8ytXrrTrTdVvzaGBBptttpnXxwcAAADAJ0FVPunYvA63/Dp11O00zVTLFEVVO6pUcz6Pv23Dhg02qHOWNBaD34cCZFtFiH/Rc4yX+7l9Lgvicx7Co+ezbob0BB8bqjnNAPVuCAAAAAD/BFWahKfLsJxgKmRS8/8FCxbYS10v9Mm+FxQmqRl+eXl5j+0Km+Kb5Kdbpqieas6yuES0XedwCh2Kwam2i/8/qGJP23V7Pn7+2VQRJvs90XY134+tIkTvnmipZNvrLNPnsrA+5yEkwdqhhx5qJ4Gm8/nnn9ulojfeeKMXxwYAAAAA3RTA6A19TdScOXOmvdT1ZMFMPk/2vaDwTJMPNf1z/vz59rKlpaVXBVe65Yfqs1bsyjxVw2mSY3t7u710quvSDQUQDQXIJCBz+/PPpopQ4gOZRFWESNwTLRV6nSGsMnpr5tvf/rY57rjjbHnlUUcdZSZNmmRGjx5tBg0aZD799FPz97//3TzzzDPmz3/+sx1qoP5rAAAAAOCVZD3GnKqn+CqvoAxzyGSaZrrAwumzVqzKPE1eje9vpe+l49ZUUi+GAmTz88+2ijDRklWFan5Ysupn9DpDVPXZmGyhfpwvv/zS/OEPfzD33HOPDdH05Gm/QJ8+ZscddzSHHHKIOeWUU8wOO+xggkb94vRLrv+T1mUDAAAA8A9VM6kyKVlA4zT7V7VXooqiRMFPkE72nf9/smEH+j8/9thjKSvX9P+tqqrqESLqa8X2pFLhhM713AwX0H2bKrj84IMPzJFHHpn2/6iKvRNOOCEvP/8wDVkIgvjHFb3OEERucqKM37LYZJNNzH/8x3/YD9EX/+KLL8yIESPskzQAAAAA5EO6HmPpqp6C3tg83bADDSfQdNNU4ivzEoWNqny76qqrzMKFCzMaLqDjSLdMVV/DGaCQbVVerj//fFQREh4VdhAJEKrhBU899ZR98tAfJz05O6Gatuk2AAAAAPBSuvAmk/2C3tg83bCDgw8+2DbXj1/uqfM1bY+tzHOqzOJ7rqloYt68eWbatGkZDRdwgqV0IZVWN+UyFMCLn7+XdP81Nzfb/ngK/HSp686qLgDR4nqRvRpE6gkrfgKonkR0m1dTWQAAAAAgkx5jbvcLKoVrNTU1SZcpZlKZl6rKzKksu+iii+wgBX2uf6vhAvq+8cshMx2GcM4555iHH344YbVdJkMB/PTzT7b0VfeFsz0Iy4sBFDFY0xNhoncbVq1aVbRpOgAAAADCS+GRKrOS9RhzemylqnoKi3TLFNMtw0tXZaZwTQHVxIkTzUsvvZRymWWmwxD22WcfW1WngE0FGuoFp2Wn+lCFXLqhAH75+Wey9LWtrc2Gm0GriARQgGDNebLTE8QPfvAD23PNoSq1119/3UyePDmHQwEAAAAA9z3GMql6CqNsmuxnWmUWPwghUaCkwgqFa6m+ppaiaj8tL33kkUcSTg4Nys8/k6Wv6nWn/egxBkRHxj3WVM6qDz2JDRkypPu602vttNNOM7/73e/ye7QAAAAAIildj7F0VU9ho75nmpSpdjwzZ860l7qerB+a2yozVZPFShSAKdRKF4zpXFHT9RL1dHOWT2bSm8wPP/9MQ8lM9wMQDn02JqqlTWHOnDnm/PPPD9WyTzdjVAEAAAAEq0orbBSeqXor/lTOqd5KFTTp36jRfrLwR33VPvnkE3PooYd291hTeNXS0pL0fk40YVSVagrVdH6V6vs5+1ZVVWW0fLKYP//Ozk47qCAdBZxUrAHRyYlcB2thRLAGAAAAIAgULCm40TTKRDINwhI14FeQJueee65ZuHBhRkGdQ6eViYYmhCmMShdKug0JAYQjJ8p4KahD716ceOKJZvTo0fYJU0/WsR8AAAAAgPxQtVayUE1ihw0ko5PFioqKXstCV65c2R2quV1m6QxNKC0ttZdOsBSm5ZOZLn0lVAOixfVUUA0uWLJkibnkkkvskwpPGgAAAABQGOmmUma6n8I1VWHEVpmNHz/eTgU95ZRTPFtmmWlPt0z3Kzbdb5Js6atzO4DocP3s9cwzz9h3P3bffff8HBEAAAAAIKFMpmhmup9TZRarurraeMnN5NB0ki03LbREoWSxjgVAAIM1lQzTlg0AAAAACk9VZFqiuWzZsoTnZU6PNe3np+WTiXq6uVk+mWhAggItfe1iVIklCiUBRJPrHmt1dXVm9uzZGTWgBAAAAAB4R0sz6+vr7efxYZRzXedsfup/naynmyrVtD1dMOYMW4ivetN1bdftAFAsrqeCDhs2zKxdu9Y+iW222Wb2yTDW6tWrTdAwFRQAAABAkDQ2NppZs2b1GGSgkEqhWibDBoohm6WcziTOrq6uhPvq9oEDBzKJE0DRciLXS0H1RA0AAAAAKB6FZzU1Nbb/tZZIejVswG/LJ50gLllopu0K3bQfSzMBFIPrYO2kk07Kz5EAAAAAADKmEM3rYQN+89VXX3m6HwAUvceavPfee+ZnP/uZOeGEE8zy5cvttr/85S/mrbfe8vr4AAAAAAAR9fbbb3u6HwAUPVh78sknzS677GJeeOEFu66/s7PTbn/ttdfMZZdd5vkBAgAAAACCb/369aapqcksWLDAXup6OkuWLDFtbW1mw4YNCW/Xdi2F1X4AEIhgTRNBr7zySvPYY4/ZJpGOAw880Dz//PNeHx8AAAAARIIa8atwob293V66nDPnayrKqKysNFOnTjUzZ860l7qu7amod9zcuXPt5/HhmnP9mmuusfsBQCB6rL3xxhtm/vz5vbaPGjXKrFy50qvjAgAAAIDI0OQ5VV6pUb9DkzMVGGkyXZApPJsxY4YdNDBp0iQzcuRIs2LFCvPKK6/Y7Q0NDQknmSpYnDhxoikrKzM33XST3VefOz755BNz7bXXmnfeeccObgCAQARrpaWl9gl/7NixPbbrSbG8vNzLYwMAAAAQEVoWGKQJl16HakuXLu21XSGbsz2o4Zp+rrNmzbIrnLT6KTYY0xJPVZvV1tbaCaexP+/YoFH/ztn/hhtusMs+nWBOVWsK5qLyWAH8Zn2En7uzXgr63e9+11x00UX2SU3vOOiJ7NlnnzXnn3+++f73v5+fowQAAAAQWtkuEwwDVWXphDQVnXsFdVmoTrjHjRtn5s2bZ1c5xdL16667zlRVVdn94oPG2Oo9Z/+f/OQndgLoSy+9ZEaPHp202g1A/kX5uTtWn40un6H1JHbGGWeYO++80yaTKk/Wpe5EbQtiMtnR0WHfAdIT+NChQ4t9OAAAAEBkOMsE409L9Ca+hD04US+11tbWtPvpZLWkpMQEjQYVaOmnQrG+fXvXdahQQ0s61VbohBNOsI+D5ubmXqGaQ7d3dXXZc7goVsYAfhH25+4OFzmRq2BNu+qdAz0x6olP/db0h2DChAlm++23N0FFsAYAAAAUnt6gV2D04YcfJrxdJ2hbbbWVaWlpCW2AokEFyf7/sXQ/qC1P0Gh1UybLWHUutu+++4Y+aATCIArP3R0uciJXS0EVrG233Xb2zquoqDCHH364+c53vhPoUA0AAABAcWj5X6pQyXljP3aZYNhoBZCX+/nNDjvs4Gq/ZJVq8TLdD4D3eO7OIVhT6a5CtFWrVrn5ZwAAAADQS7reYm73C6LBgwenDc0GDBhg9wuigQMHutov7EEjEAY8d+c4vGDu3LnmggsuMG+++abbfwoAAAAA3TRBzsv9gkhLptL9/zRJ0+lbVEyqQtFSTS1f1WUmXYWc4DDZvtoeGxyGPWgEwoDn7hyHFwwbNsysXbvWlt7qXYVNN920x+2rV682QUOPNQAAAKB4fXqWLVuWMHgJQ5+eTOlcRNUdsUscFSApVMukR1kxjk8BmE6c0x2fM+UzGbUZiv0abvcHUFhReO7ucJETua6fraury+XYAAAAAMDSCVd9fb2dLKcTsdgTNKdCS+cfQT0xc0MncDp5W7NmjQ2vFFqpKssPlWrJgi4dp7M9VdDl3JZpcOh2fwCFxXN3jhVrYUTFGgAAAMJYUaDG0QonVFU0ZcoU357kNDY2mlmzZvVohq2qJJ2YTZ8+vajHFnU6XWxubk45LECBV1VVVdoQUF/LTXDodn8AhRXm5+4OFzlRTsHaunXrzFdffdVjWxCDKYI1AAAAhP1kR8tyVGHg15OdIAWBUaJeaq2trWn307KwkpISExWEfkC4n7s78hms6cnjoosuMvfee2/C6aC6U4OGYA0AAABhCtW0PCf+Zb5z0t/Q0ODbcA3+o0EFsQFtMgpuS0tLQ3uSnW2/uSjcH0AYucmJXE8FvfDCC80TTzxhbr75ZrPJJpuY3/zmN2bOnDlm9OjR5q677srluAEAAADkQCfxqlRL9N65s622tjaQb4ajONJN6IzdT6GuKtemTp1qZs6caS91XdvDwuk3F7801uk3p9sdUbg/AGRRsbb11lvbAK26utqmdosXLzbbbbed+X//7/+ZBQsWmD//+c8maKhYAwAAQBg0NTXZk/d0Fi1aZF/Pw3thq1DKtMfaW2+9FfpKSTf95u67777Q3x9AmHXks2Jt9erVZptttrGf64vruuy3337mqaeeyvaYAQAAAORIYY6X+8GdMFYoKQhSQJjKqFGjIlEp6fRUS6Wrq8t8/vnnkbg/AGQZrClUa2lpsZ+PHz/e9lqTBx980K6pBwAAAApJJ6eq1NLqCV1G+WQ1XQDidj+4720X349s2bJldnuQwzVVbWjSX/yyUFVnaftrr72Wsg+bwiQtk1QlX5ClC9Ucb775ZiTuDwD/ktmC+Rg//OEP7RPnAQccYGbPnm2OOuooc8MNN9hkft68eW6/HAAAABCp6Zf5pGWH+v8rzElULaPqI92u/VC43na631WhVFNTE9hloQrXtGIp0STMqFRKZtpvbuXKlZG4PwBkGaydc8453Z8fdNBB5u233+7us7brrru6/XIAAACAp9MvnQqhKPYwUmijUFH/fwUesfeN09uprq4usOGOX6nyKNMKpSD3ttNjqKSkJLKVkgoSFa6l67GWad/uoN8fALJcChpPPQP0goVQDQAAAIXC9Mvk9NpcoWJ5eXmP7apUi2LYWAhRqdhKVynphLfxtF1LRoNeKZlJv7mysrLI3B8AcgjWFi5caI488kiz7bbb2g99/vjjj2fzpQAAAIC8VghFkcKz1tZWO/1z/vz59lJ9kgnV8iMqFVvpKiUlPkwKW6Vkun5zuj1K9weALIK1m266yRx66KFmyJAh9l1CfajU9fDDDzc33nhjfo4SAAAAiBH1CqFM6KRdyw5POOEEe8lJfP5QoRStSkmFZ+PGjbOrt/T/02VVVZXdHsX7A4i6PhsT1c+noCcCDS0488wze2xXqPaLX/zC9rQImo6ODvsk+Nlnn2W8Hh4AAADFo+mfU6dOTbufKrWC3NMKwev5J4l620UlTNHya1WKKtRWhZ7CxCiHutwfQDC5yYlcB2tqVvnqq6/aYQWx3n33XTNhwgTT2dlpgoZgDQAAIHgnq6oSSTf9UssfOYlFMafUqlJNy/6iEKoBQFi4yYlcLwU9+uijzX333ddr+/333297rQEAAAD5Rg8j+BG97QAgelxXrF155ZXml7/8pdl3333NPvvsY7c9//zz5tlnnzXnnXdejyTv7LPPNkFAxRoAAEAwUSEEAAACtRR07NixGe2ndwrff/99EwQEawAAAMFFD6P80ynDmjVrzNdff22nIQ4ePDhpo34AAILOTU7Uc0ZwBlTKDAAAAPht+iXyQycVCi0VqjkUrinEjJ2CCMB/eOMByD/XwRoAAACA6IRqS5cu7bVdIZuznXANCM5SeQ11UX9KlsoD3nE9vAAAAABANJZ/qsollba2toRTWQH8i34/Ojs7TXt7u70s1O+LQrUZM2b0CNVEk5S1XbcD8IbrHmthRI81AAAAoCeFAJpwmU5lZaUpKSkpyDEBQVKsZdRa/qnfy/hQzaH+iKpcU5snloUCuedEVKwBAAAA6CU2DPBiPyCKy6jjfz+cZdS6PV/UUy1ZqCaqrdExaD8AuXMdrC1ZsiRh+aq26TYAAAAAwafKGi/3A6Ki2Muo031vt/sB8DhYGzt2rFmxYkWv7atXr7a3AQAAAAi+wYMHpw3NBgwYYPcD8G9r1qxJW8nZ1dVl98sHLTX1cj8AHgdrStW1JjtRD4ZBgwa5/XIAAAAAfEiv+dOdeJeVlSU8NwCirNjLqKdMmWJ7qCX73dT2iooKux+A3GVct33uued2/xJecsklZrPNNuvRHPGFF14wu+++uweHBAAAAMAPnAbr8Q3YVammUC2fDdiBoCr2MmoNJKivr7fTP3X+Hrvk1Anb6urqGFwAeCTj3+RXXnnFXuqX8o033jADBw7svk2f77bbbub888/36rgAAAAA+IDCM01Ec5a3KQzQ8k8q1YpD52P8LIKxjDpVRVq+l1FPnz7dNDQ0mFmzZvUYZKBKNoVquh2AN/psdNkx8Yc//KFNv9ONGw3rGFUAAAAAKAadr8RXDyrA0ZJdqgf9ORU0GS3FLMTPTKvLNP1Tjxs9TrT8k0o1wNucyHWwFkYEawAAAAD8zC9BDXILQllGDYQvJ3K9qFtlx3PnzjULFy40y5cvNxs2bOhx+/vvv+/+iAEAAAD4FlUvxaVaCN33qbS1tdmTP5aF+gfLqIFocB2snXrqqebJJ580J554ov2jypMCAAAAEN4eWY2NjQn7NKk9DH2aCsN5vKTS1dVl9yspKSnYcSE9/W7zMwnf8yKQU7D2l7/8xfzpT38y++67r9t/CgAAAERa0HpkKVTTZMH47jHLli2z29UcnXAt/9KFam73A/wkaM+LQLy+xqVhw4aZ4cOHu/1nAAAAQKQ5PbLiww9d13bd7rfln6pUS9SS2dlWW1tr9/MbHVNTU5NZsGCBvfTjMbqhkMHL/QC/CNrzIuBJsHbFFVeYSy+91Kxdu9btPwUAAAAiKdMeWX6aK6aearHLP+PpWHXiq/38VmVXWVlppk6dambOnGkvdV3bg0rL4tKFZmqKr/2AoAji8yKQiOu3NK677jrz3nvvmS222ML+gdITeKzFixe7/ZIAAABAqAWxR1a6E163+xVCWJeuqteUlsWlmgqqSZP0pEKQBPF5EfAkWDvmmGPc/hMAAAAg0oLYI0tBjpf7FXvpqkInLV2tqakJ5ERTp9dUfC8qFTooVKMXFYI2RTiIz4uAJ8HaZZdd5vafAAAAAJEWxB5ZOhnW9E9VeyUKqxRU6XbtF7Slq9XV1SaIFJ4NHTo0MtMTMw1oEMwpwkF8XgQ86bEGAAAAIPw9shRg6GRY4oMb53pdXZ1vgo4gLl3Nhu57LYsrLS21l2EN1cLYKy/snKXY8QG3sxQ7/mcXxOdFwJNgTe8a/PKXvzR77rmnLTnWhNDYDwAAAACJe2Sl4sceWaowUV+y8vLyHttVgeK3fmVBW7oK7wIaBHOKcFCfF4Gcg7U5c+aYefPmmeOPP96Ovj333HPtH9S+ffuan//8526/HAAAABAJWsZXUVHRq0JDFRna7tceWXqt39raahYtWmTmz59vL1taWnwVqsUuXU12Eq7tup/9snQV3gU0SE73WWdnp2lvb7eX+Zqwme0U4aA+LwKxXC9W/v3vf29uu+02c8QRR9gg7YQTTjDbbrut2XXXXc3zzz9vzj77bLdfEgAAAIiEoPbI0nJPv/clc5auqqJJ92dsgODHpauIbq+8QlEhTPywCz3nqErM68Aql6XYQX1eBLKuWGtrazO77LKL/Vxr+vXLKkceeaT505/+5PbLAQAAAJES5B5ZqhJqamoyCxYssJd+qxoK0tLVYlQVBUFUeuXlm87TFUDGT9TUdW13zuP9shQ7yM+LgOtgTX+UnCcxVao9+uij9vO//e1vZpNNNvH8ALWO/j/+4z/MiBEjzKabbmpDvZdeeqn7dv3RufTSS+0vqG4/6KCDzLvvvuv5cQAAAABRFpRm8kFZuupQwNHc3GyPWZVautR1r4OPoKBXXu50jpwueFTBjJcBLkuxEWWug7Vjjz3WLFy40H5+1llnmUsuucRsv/325vvf/745+eSTPT24Tz/91Oy77752ffVf/vIX8/e//91cd911ZtiwYd37XHvtteb66683t9xyi3nhhRdsyeghhxxi1q1b5+mxAAAAAFEVtGbyztJVta3RpV+Xfxa6qigICGhy5yypTKWrq8vuF9UpwoCX+mzMMaZWX7W//vWvNlw76qijvDsyY8zs2bPNs88+26vBoUOHPnr0aHPeeeeZ888/327TH58tttjC3Hnnnea73/1uRt+no6PDruvWv9XabgAAAAD/ouWeqkxL1vdKJ80KQlQVxklz5nQuo8q0VAGICgyqqqoityzOCXIlUa88Py/r9QMtKU7Vp86h31stvfT6Z6fhE7HfX0GoQjV+ZggSNzmRq4o1pdqqStMfTcfee+9tJ4N6HarJAw88YCZNmmS+/e1vm1GjRpkJEybYwQkOHYdKWLX806H/+F577WWee+65pF/3yy+/tHdS7AcAAAAA76b9wX9VRUER1F55fhE/YTPX/cK8FBvwgqvfJL1j8sc//tEu/yyE999/39x88802uPuv//ov28dNU0cHDhxoTjrpJBuqiSrUYum6c1siV199tZkzZ07ejx8AAAAIOprJ50e6UM3tfmGjIKampsYGtnpsqaealn9SFZme2iMpNEtXDan9ojpFGChqj7VjjjnG/N///Z8phA0bNpiJEyeaX/ziF7Za7bTTTjM/+tGPbD+1XPz0pz+15XzOh95hAwAAANAbzeTDV1UUFEHplec3WjKb7vexrKwsckuMgXxx/SytXmqXX3657X22xx579Eq5VVHmFT0Z7Ljjjj227bDDDrZqznkykE8++aTHE4eu77777km/rqaX5mOCKQAAABDWZvIaVJCoPbPTY41m8sGqKkK4qUWSqNov9jGmx5TOo53bARQhWLv99tttg8OXX37ZfsT/UfUyWNNEUDX0jPXOO++YMWPG2M/Hjh1rnxQ0pdQJ0tQvTdNBTz/9dM+OAwAAAIgqZ9qfmsnr9X6iZvJM+8u+qijV6hmqipALhWdquu7081OQq6CWxxRQ5GAtdnBBvp1zzjlm8uTJdinod77zHfPiiy+aW2+91X6InhBqa2vNlVdeaSvpFLSp/5smhWrJKgAAAADvmsnHT/tTpRrT/rJHVRHyTefMJSUlxT4MINT6bExUz+0jDz30kO2J9u6779rgTIMM1GfNocO/7LLLbNimscL77befuemmm+xY6nyMUQUAAACiav369TSTzwOd01BVBAD+4SYnch2sKdhK+IX69DGDBg0y2223nZ3eMnz4cBMUBGsAAAAAAADIe7A2depUs3jxYvtu1bhx47r7numdqvHjx9ueaArZnnnmmV6DB/yKYA0AAAAAAABuc6K+xiVVox100EHmo48+6h5goD4L3/rWt+wYZE0L2n///W1/NAAAAAAAACCsXFeslZeXm8cee6xXNdpbb71lDj74YBusqaJNn69cudIEARVrAAAAAAAAyHvFmr7o8uXLe21fsWKF/cZSWlpqvvrqK7dfGgAAAAAAAAiMrJaCnnzyyea+++6zS0D1oc9POeUUc8wxx9h9XnzxRVdTOQEAAAAAAIDQLwXt7Oy0/dPuuusuOw5aNBL6pJNOMr/61a/saOhXX33Vbt99991NELAUFAAAAAgeDVR7+umnzccff2y23HJLM2XKFDtUDQAA304FjQ3Y3n//ffv5NttsY0pKSkxQEawBAAAAwdLY2GhmzZplV9A4ttpqK1NfX2+mT59e1GMDAARbQYK1MCFYAwAAAIIVqs2YMcPEn8r06dPHXjY0NBCuAQD8E6zpj9Kdd95pv1i6P1D6Ixc0BGsAAABAcJZ/VlZW9qhUiw/XVLnW0tLCstAM6ZRwzZo1ttWP2vyovY8TUgJAFHW4yIn6Z/IF9cWcJ1Z9DgAAAADF6Ke2cOHCpKGaExItXbrU7ltdXV3QYwwinTSqR53TP1sUrqlnHed+AJBeRsHab3/724SfAwAAAEAx+qmlo7AI6UM1hZDxFLI52wnXAMCDYA0AAAAA/NRPLR1VXCE53Z/pwse2tja7BIploQCQXF/j0qpVq8wZZ5xhdtxxR/ONb3zDDB8+vMcHAAAAAHi1/FOVam5CNYVAFRUVZsqUKXk9tqBzeqql0tXVZfcDAHhYsXbiiSeaf/7zn+aUU04xW2yxBe9eAAAAAMgL9Ulzs/zTOTepq6tjcEEa6UI1t/sBQFT1z+aP2zPPPGN22223/BwRAAAAAGTRJ03TQBWqTZ8+PW/HFBYaUODlfgAQVa6fJcePH2+++OKL/BwNAAAAgIymYyp0Uh8xLXkMa3VWpn3Sfvazn5lp06aF+r7w2uDBg21olqoibcCAAXY/AEByfTa67AL6t7/9zcyePdtceumlZuedd7ZPtrHU3DJoOjo67LQbTcUJ4vEDAAAgutMxVaVVX18fyiothYiVlZVm2bJlCfusaemn/v8tLS0Eah5OBXWoVx1TQQFEUYeLnMj18ILS0lL7DQ488EAzatQoM2zYMPuh7boEAAAAkL/pmPE9xxQ6abtuDxuFZQoNJb63M/3UcqeTRoVn8cs9VTxBqAYAeapY23PPPe0Tr94pSzS84IADDjBBQ8UaAAAAglC5layRfxAqt3JZwpqoUk/BD/3UvKFTQmdKqM71tPyTIXUAoqzDRU7kOljbbLPNzCuvvGLGjRtnwoJgDQAAwHtR6gWWb01NTWbq1Klp91u0aJGprq42YVzCyuMJAODHnMj18IJJkybZdfhhCtYAAADgraj1AvPLdEy3UzQLuYQ1/v18ZwlrQ0NDRo8JhWh+DA0BANHmusfaWWedZV8k3Xnnnebll182r7/+eo8PAAAARFsUe4H5ZTpmpvsViqrMdO6QaJGMs622ttbuBwBAELleCtq3b+8sTuvv9WV0GcQ/iiwFBQAA8EYYeoH5UVCnYwZ9CSsAIJo68rkUVH+sAQAAgETUAytZqCYKhdRWRPsRpLifjqmKP+dN7SBMxwzyElYAADLhOlgbM2aM238CAACAiCBIyR/1IVM/skS96/w6HTOoS1gBAMhbsCYfffSReeaZZ8zy5cvNhg0betx29tlnZ/MlAQAAEAIEKfml8KympiYw0zF1bAr+0i1h1X4AAESix5qGFvznf/6nGThwoBkxYkR36bn9Yn36mPfff98EDT3WAAAAot0LDPkfZiGJlrBmOhUUAAA/5kSup4Jecskl5tJLL7VfvLW11b4ocj6CGKoBAADA+15gEvsGrN97gSH/S1jLy8t7bFfAGtVQTQG0BjssWLDAXgZxABwAIMuKNVWpvfjii2bbbbc1YUHFGgAAgPdVSvG9wCoqKnzbCwz5p/AoKEtYC/27oZBRgTS/GwAQvJzIdbB24YUXmuHDh5vZs2ebsCBYAwAA8B5BCpB4WWz8KRjLYgEgQsGaXiAdeeSR5osvvjC77LKLGTBgQI/b582bZ4KGYA0AAABAIfoPxlaqxaL/IBAtimLWrFljvv76a9O/f38zePDgXi0UEIycyPVU0Kuvvto88sgjZty4cfZ6/PACAAAAAEBPqt5MFqo5J9lLly61+1VXVxf02AAUlsIaVXMrVHMoXFN1t8IcBIvrYO26664zd9xxh/nBD36QnyMCAAAAgJDRSbSX+wEIbqimED2eQjZnO+FasLieCrrJJpuYfffdNz9HAwAAAAAhpEoUL/cDEDyqTE0Xnre1tfXqw4iQBWuaYPPrX/86P0cDAAAAACGk4R3qoZasfY62a3Ku9gMQTk5PtVS6urrsfgjxUtAXX3zRPPHEE+ahhx4yO+20U6/hBZp0AwAAAAD4Nw0kqK+vt1NBFaLFVqQ4YVtdXR2DC4AQSxequd0PAQ3WSktLGQENAAAAAC7pPKqhocGuAoodZKBKNoVqnGcB4aYBBV7uB3/os5HFu67GqAIAAABALtavX2+nf6rXknqqafknlWpA+Cl+aW5uTlmRplWBVVVVSZeNw385ETEoAAAAEJETOqe/j6ohBg8ezIlbkShEq66uLvZhACgwPecqTE80FdRRVlbGc3PAZBSsTZw40SxcuNAMGzbMTJgwIeUPefHixV4eHwAAAIAc6R13VUfFVkkoXNMJnt6RBwAUhvOcG/+crEo1hWo8J4c0WKupqTGbbLKJ/fyYY47J9zEBAAAA8DBUS1QdoRM6ZzsncgBQOHrO1fJCqojDgR5r9FgDAABASNHPBwCA/OZEfbP4+gAAAAACwKmGSKWrq8vuBwAA3GN4AQAAABBS6UI1t/sBcIehIUD4EawBAAAAIaUTeS/3A5A5hoYA0cBSUAAAACCkVB2TLjRTjzXtB8D7oSHx1aDO0BDdDiAcCNYAAACAkNKSM1XHpFJWVsbSNMDj5Z+qVEulra3N7gcg+FzXfK9fv97ceeedZuHChWb58uVmw4YNPW5/4oknvDw+AAAAADlwlpzFL0lTpZpCNZakAcUbGlJSUlKw4wLgk2Bt1qxZNlg74ogjzM4778y7WwAAAIDPKTwbOnQoTdSBAmBoCBAtroO1u+++29x7773m8MMPz88RAQAAAPCcQjSqY4D8Y2gIEC2uf5MHDhxotttuu/wcDQAAAABEiPpsUUkYzqEhqSrSGBoCRHh4wXnnnWfq6+tptAgAAAAAOdBkyObmZtPa2mo+/PBDe6nrTIwMNoaGANGSUcXa9OnTew0o+Mtf/mJ22mknm7THamxs9PYIAQAAACBkFJ4tXbq013ZVOTnbGSwRXAwNAaIjo2At/pf+2GOPzdfxAAAAAECoafWPApdU2tra7MAJqpqCi6EhQDRkFKz99re/zf+RAAAAAEAEOEFLKl1dXXY/Bk4EG0NDgPBz3WPtwAMPNO3t7b22d3R02NsAAAAAAMmlC9Xc7gcACNBU0KamJvPVV1/12r5u3Trz9NNPe3VcAACgANavX2//fmtJkhotT5kyxfTr16/YhwUAoaYlgV7uBwAonoyfqV9//fXuz//+97/bNf+xL8offvhhU15e7v0RAgCAvNDAoVmzZtlJdI6tttrKTv+OH1wEAPCO+mwpNEtVkaYm99oPABCSYG333Xe368P1kWjJ56abbmp+/etfe318AAAgT6HajBkzbAPtWMuWLbPbGxoaCNcAIE90TqUq4URTQR2aHEmTewDwvz4b419RJ/HBBx/YF9/bbLONefHFF83IkSO7bxs4cKAZNWpUYJeOqD+cJrZo5LWmtgAAEGaqNK+srOxRqRZLJ3KqXGtpaQns33YACAKdf2gpfmzlmirVFKrp/AQA4P+cKOOKtTFjxtjLDRs25H6EAACgaNRTLVmoJnojTVUU2q+6urqgxwYAUaKTNp2wOVNCtTxUyz+pVAOA4MgoWHvggQfMYYcdZt890eepHH300V4dGwAAyANVR3i5XzEpBOSEFECQ6TmrpKSk2IcBAMhnsHbMMcfYYQVa7qnPU/1R0PISAADgX+rr4+V+flpCpXBNx80SKgAAAPgmWItd/slSUAAAgm3KlCm2h5oGFSRqter0WNN+fg7VEjX9VsjmbCdcAxA1sVW8XV1d9s0GZ7poIap5qSJ2h/sLCIeMe6w51q1bZwYNGpSfowEAAHmngQT19fV2+qdewMeGa84L+rq6Ot8OLtDxplumqkp79S3iBAVA0GgFkHpc6nlOFbh6kyOT5+NEVbyFrOalitgd7i8gPPq6/QelpaVm//33N5dccolZuHCh+eKLL/JzZAAAIG+mT59uGhoaTHl5eY/tqlTTdt3uV867+6moUkP7AUCQNDY22qnNU6dONTNnzrSXuq7tmVTxJntudKp5tV8+JPv++f6+QcX9BYRLn42J1oCk8Mwzz5innnrKNDU1mb/+9a/2l3/SpEnmgAMOsJPDvvWtb5kwj1EFACBMsq2MKKb29vaUU01jQ0K9IQgAQXi+U3imSuL40zOn8jbZmx7av7m5Oe0bDqJloVVVVZ5W82by/fPxfYOK+wsIX07kOliLpSeDv/3tb+Z//ud/zO9//3vbfy2IwwsI1gAACI7Ozk7T2tqadj9VeTBpD4ATWs2aNatHKK/wXcvi/VChq3MoPWcle9PA6X3Z0tLSKwzM9DkxX8+NPCe7w/0FhC8nct1jTd555x1bseZ8fPnll+bII4+0FWsAAAD5pObO6kOT7t1+7QcAySrBNMBF2/2w/F2VdKkqcXXsWiKo/eLPuTKpVMtlf6++ntffN6i4v4DwcR2sqReL+qrpCV0fF110kdl1110pUwUAAAWh1xxaxpVoKqijrKyM1yYAbCWYKtUSLdLRNj1P1NbWmpqamqIuC003kCXVfnqjwQ23+3v19bz+vkHF/QWEj+vhBSNHjjRr166107b08cknnzDAAAAAFJRK8ysqKnqdeKhSTdsLMVFNJ+yq3F+wYIG9DGI7DCDs3FSCFZPeLMh2P6eKNxP5qObN5PtTRfxv3F9A+LgO1l599VUbqM2ePdsuAf2v//ov841vfMNMnjzZXHzxxfk5SgAAgDgKz8aNG2f70Kj3kC7V7LkQoVq2k/sABKcSrJA0SEHPY8kqbbVdbxpov0S3ZRrM5aOaN5PvTxXxv3F/AeGT0/CCVatW2Xdo77//fvtuLcMLAABA2GU7uQ9A4elcRcF3OosWLSp6v2jnuUVin18yfW7RuYwCwkS9uVQBpbAmn288JPr+hfi+QcX9BUR4Kqie8J2hBX//+9/N8OHDzX777Wf/EB1wwAFmt912M0FDsAYAAPI9uQ9A8X5nNagg0WmP335nE00vVaVaXV1dRoG9/o9r1qyxYU1XV5ddcugsKyxEBVTs99f3HjRokHnmmWdsgKQqLVXc+eF+9ov4+6tQPycARQ7WRo0aZfbff//uIG2XXXYxQUewBgAAwlb9AsCbSrBihIHq+Rb0MCpRSKgQs76+3lf3NwDkmhO5HjWyfPlyt/8EAAAgFILSrwnAvynEUXiWKOTJtBKskBSiBT2YT7ZkXpWD2u63MBMAitZjLSyoWAMAAJmgYg0IrrBUgvkdS+YBhEFel4KGEcEaAAAIY78mACg03oAAELWcqG/BjgoAACDgFJapP5DEN5h2rmtpGaEagKhiyTyAqCFYAwAAyKJfU3l5eY/tqlSjbxCAqNMyWy/3AwC/y3kpqMrjnnjiCTNu3Dizww47mCBiKSgAAHAriv2a9LJxzZo15uuvvzb9+/c3gwcP7lW5BwQBj+X8Yck8gDDI61TQ73znO2b//fc3Z555pvniiy/MpEmTTGtrq33SvPvuu81xxx2Xy7EDAAAEQhgm97mhF5YKERVEOBRIKFTUC08gKHgsF2bJvKZ/KkSLDddYMg8gjFwvBX3qqafsO7Jy33332SfK9vZ2c/3115srr7wyH8cIAACAIgcRS5cu7RFEiK5ru24HgoDHcmGwZB5AlLiuWNMfm+HDh9vPH374YVuhttlmm5kjjjjCXHDBBfk4RgAAABSJ3kRN12S8ra3NLpNgKR38jMdyYSk8q6mpidySeQDR4zpYq6ioMM8995wN1xSsafmnfPrpp2bQoEH5OEYAAAAUidOHKpWuri67X0lJScGOC3CLx3LhRW3JPIBoch2s1dbWmu9973v2j82YMWO6nyi1RHSXXXbJxzECAACgSNIFEW73A4rFT49lhicAQISDtZ/85Cdmr732MkuWLDHf+ta3TN++/2rTts0225irrroqH8cIAACAItFJv5f7AVF/LDM8AQAiPrzg8ssvNzvssIM59thje5RIH3jggebxxx/3+vgAAABQRKqkSRc0DBgwwO4H+JkfHssMTwCA8HEdrM2ZM8d0dnb22r527Vp7GwAAAMJDy9NUSZNKWVkZy9gQusfy+vXrTVNTk1mwYIG91PVCDE/QfgCA4HBd56wn+kQvnF577bXuaaEAAAAID2d5WvzyNVX3KIhg+RrC9lhubGw0s2bNMh9++GH3PltttZWpr6+30y6zwfAEAIh4sDZs2DAbqOmjqqqqR7imd29UxfbjH/84X8cJAACAIlLgMHToUBquI/SPZYVqM2bM6FU5tmzZMru9oaEhq3DNT8MTkmGoAgC412djhrXG//u//2ufaE8++WRTV1fX453JgQMHmsrKSrPPPvuYIOro6LD/H/U00B9ZAAAAANGjggGd18RWqsVSyKTKtZaWFtOvXz9XX1uFCK2trWn30/cvRsUaQxUAILucKOOKtZNOOslejh071kyePNmWSwMAAABAWDz99NNJQzVRoYGGDGi/6urqrIYnpKpIK9YgEGeoQjxnqIIQrgGARz3WDjjgALNhwwbzzjvvmOXLl9vPY+2///5uvyQAAAAAFF264QJu90s0PCFRgFXMQSCZDlVQxQbLQgHAg2Dt+eefNzNnzjQffPBBr74DeqLNdVoOAAAAgOzp9bgqqhSWKMiZMmWK62WLUZVuaqjb/YIwCIShCgBQ4GBNAwomTZpk/vSnP9k/KLxrAQAAAPhDPqZZRolCSN1fGlSQqBW102NN+4VlEEgQhioAQKiCtXfffddOwtluu+3yc0QAAAAAXMvXNMsoUWWfQkjdXwq6Yu9LJ/jSILdcKwD1tfxS/aVgz8v9ACBq+rr9B3vttZf55z//mZ+jAQAAAJDV8k9VqiWqsnK21dbW0rYlAwofFUKWl5f32K5KtTCGk85QhVSKNVQBAILA9dsOZ511ljnvvPNsA8tddtml13TQXXfd1cvjAwAAAFDEaZZRpPCspqYmEr3q/DpUAQBCG6wdd9xx9vLkk0/u3uaUSTO8AAAAAAjXNMuoUogWlRDSj0MVACC0wVpLS0t+jgQAAACAL6dZIvz8NlQBAIKiz8ZEjRgipqOjw/4h+eyzz+wfEwAAACBItGqksrIy7TRLvUkexuWMAAAUKydyPbxA/t//+39m3333NaNHjzYffPBB93Sc+++/P7sjBgAAgG8Dm6amJrNgwQJ7SdsPf95nzjRLia8w8nKaJfKP3zkACBbXwdrNN99szj33XHP44Yeb9vb27if60tJS+8caAAAA4dDY2GiroKZOnWpmzpxpL3Vd2+G/+yxq0yzDiN85AIjAUtAdd9zR/OIXvzDHHHOMGTJkiHnttdfMNttsY958803b3HPlypUmaFgKCgAA0JNO5GfMmNFrWaFT/URQ49/7TG98R2GaZSr6GQStV5hfHj8AAOMqJ3IdrG266abmH//4hxkzZkyPYO3dd981u+66q/niiy9M0BCsAQAA9O7X9eGHHya8nX5dvXGf+Yde08dPt1S4ppDRr9MtefwAQIR6rI0dO9a8+uqrvbY//PDDZocddnD75QAAAOAzqnZKdoIvel926dKldj/8C/eZP+gESPdzbKgmuq7tut2PePwAQHD1d/sP1F/tjDPOMOvWrbNP8C+++KJtrHn11Veb3/zmN/k5SgAAABSMqn283C8KuM+KT+cm6e7ftrY2W3ngt2WhPH6QD0FcEg1EIlg79dRT7XLQn/3sZ2bt2rW2qaamg2oK0Xe/+938HCUAAAAKRkvmvNwvCrjPis8JEFLp6uqy+5WUlBg/4fEDrwVxSTQQVK57rMVSsNbZ2WlGjRplgoweawAAAL37PS1btqxXI3Wh31Nv3GfF197ennI5pUM/h9LSUuMnPH6QjyXRyVRUVBCuAcXssRZrs802C3yoBgAAgJ504q7VCBK/bMi5XldXxwl+DO6z4lM1jpf7FRKPHxR6SXQO9TUAsqlYmzBhQsZrsRcvXmyChoo1AACA3hobG82sWbN6VAGp0kEn+NOnTy/qsfkV91nx6LSmubk55XLQAQMGmKqqKt/2meLxg1xpRVlra2va/VQh6bcl0UBQc6KMgrU5c+Z0f66hBTfddJPZcccdzT777GO3Pf/88+att94yP/nJT+wQg6AhWAMAAEi+RE2TCFUBod48U6ZMoWomDe6z4gnDEjgeP4jqkmgg1MFa/PACPcFfccUVPbZfdtll9o/YHXfcYYKGYA0AAACFQnBS+KbtqlQrKyvzfagG5IqKNSAAwZq+8EsvvWS23377HtvfffddM2nSJPtNg4ZgDQAAAMVa6qfKEfXXYqmfd3SK40wJVU+1wYMH+3b5J+ClMCyJBkI/vGDTTTc1zz77bK/t2jZo0CC3Xw4AAACITKg2Y8aMXsu0NAlS23U7vKHAQNU4WuqmSwIERIUe66qETUXVm/xOAN5xPRKntrbWnH766XZIwZ577mm3vfDCC3YJ6CWXXOLhoQEAAADhWKKpr61KtUSLRbRNJ7l6nV1TU8OyUAA5cZY8syQaKAzXS0Hl3nvvteXqb7/9tr2+ww472BcK3/nOd0wQsRQUAAAg2vK9RLOpqclMnTo17X6LFi0y1dXVOX8/AGBJNFCYnMh1xZooQAtqiAYAAAAkWqIZ/36zs0SzoaEh53BNlSNe7gcAmS6JBpBfWQVr8tVXX5nly5ebDRs29Ni+9dZbe3FcAAAAQN4Vaolmup5HbvcDAAD+4Hp4gaZ/qt+EhhiMGTPGjB071n5oXK8uAQAAgKBQT7X4YQLx4drSpUvtfrnQ62ctLU22DEvbKyoq7H4AACDEFWs/+MEP7Prshx56yL6jxhptAAAABFWhlmiq2k392rS0VK+fYyvknNfTdXV1DC4AACDswdqrr75qXn75ZTN+/Pj8HBEAAABQIIVcoqk+berXlmhIgkI1L4YkAAAAnwdrO+64o1m5cmV+jgYAAAAoIGeJpgYVJOqzpmoy3e7VEk2FZ+rXpqWlqoJTYKevTaUaAAARCdauueYac+GFF5pf/OIXZpdddjEDBgzocXu6MaQAAACAXxRjiaa+VnV1tWdfDwAAFE+fjYnemkuhb99/zTuI763mTE3SZKWg6ejoMJtvvrn57LPPCAYBAAAiqLGxsdcSTQ0TCNMSTb1Op1IOAABvcyLXFWuLFi1y+08AAAAAXwv7Es1EwaGWuKpaLyzBIQAAgahYCyMq1gAAABBWCtW01DX+Zb+zAkUDFQjXAADILifKKlhrb283t99+u3n77bft9Z122smcfPLJ9psGEcEaAAAAwkjLPysrK3tUqiUaztDS0hKa6jwAAAqZE/2rYZoLL730ktl2223Nr371K7N69Wr7MW/ePLtt8eLFuRw3AAAAAA9paWuyUE30HvvSpUvtfgAAwD3XPdbOOeccc/TRR5vbbrvN9O//r3/+9ddfm1NPPdXU1taap556KovDAAAAAOA19Yvzcj8AAJBjsKaKtdhQzX6R/v3NhRdeaCZNmuT2ywEAAADIEw1h8HI/AACQ41JQrS1dsmRJr+0qIR8yZIjbLwcAAAAgTzTZVD3UnEEF8bS9oqLC7gcAAAoQrB1//PHmlFNOMffcc48N0/Rx991326WgJ5xwQhaHAAAAACAfNJCgvr7efh4frjnX6+rqGFwAAEChloL+8pe/tH+Ev//979veajJgwABz+umnm7lz52Z7HAAAAADyYPr06aahocHMmjWrxyADVbIpVNPtAAAgO302ahRQFtauXWvee+89+7kmgm622WYmCmNUAQAAgCBav369nf6pQQXqqabln1SqAQCQW07kumLNoSCttLS0+3MAAAAA/qUQrbq6utiHAQBAtHusafnnJZdcYpO7yspK+6HPf/azn5murq78HCUAAAAAAAAQ9GDtrLPOMrfeequ59tprzSuvvGI/9Pntt99uzj77bJNP6uGm/m61tbXd29atW2fOOOMMM2LECFNSUmKOO+4488knn+T1OAAAAAAAAADXS0Hnz59vp4Aedthh3dt23XVXO6ZbU0Fvvvlmkw9/+9vfzP/8z//Y7xXrnHPOMX/605/MH/7wB1s5d+aZZ9oGrM8++2xejgMAAAAAAADIqmJtk002scs/440dO9YMHDgwL/dqZ2en+d73vmduu+02M2zYsO7taiKnSrl58+aZAw880Oyxxx7mt7/9rfnrX/9qnn/++bwcCwAAAAAAAJBVsKaKsCuuuMJ8+eWX3dv0+VVXXWVvywct9TziiCPMQQcd1GP7yy+/bPu6xW4fP3682Xrrrc1zzz2Xl2MBAABAOG3cuNG+odve3m4vdR0AAMDTpaDqqbZw4UKz1VZbmd12281ue+2118xXX31lpk2bZpdhOhobG02utOx08eLFdilovLa2Nlsl50wndWyxxRb2tmQUBMYGgxqjCgAAgOjSSoiPP/7YDupy9O/f32y55Za23QiKT0HnmjVr7M9IP5vBgwfb/ssAAAQqWFOIpQEBsdRfLR+WLl1qZs2aZR577DEzaNAgz77u1VdfbebMmePZ1wMAAECwQzW97oynAMfZTrhWXASfAAC/6rPRxzXu//d//2eOPfZY069fv+5t69evt+9M9e3b1zzyyCN2Geinn37ao2ptzJgxdnKoBhtkWrGmcFB/sIcOHZrn/xUAAAD8Qi+Fm5ubewQ28QYMGGCqqqqojvJZ8OnQ63jCNQCAl5QT6W9LJjmR64q1L774wr4A2Wyzzez1Dz74wNx3331mxx13NAcffLDxkpaWvvHGGz22/fCHP7R91C666CL7R1QvdLQ01ami0wujJUuWmH322SflAAZ9AAAAINqcpYWpqKev9ispKSnYceFfdN6hSrVU1AJGJz0EnwCAYnAdrNXU1Ng+aj/+8Y9tY9c999zT9jlbuXKlnc55+umne3ZwQ4YMMTvvvHOPbeqlMGLEiO7tp5xyijn33HPN8OHD7R/Us846y4Zqe++9t2fHAQAAgHBKF6q53Q/eIvgEAIRuKqgGCUyZMsV+3tDQYMrKymzV2l133WWuv/56U2i/+tWvzJFHHmkr1vbff397PF4MTQAAAED4qU+Xl/vBWwSfAAC/c/0KYe3atbaSTB599FFbvaZ+Z6oQU8CWb01NTT2ua6jBjTfeaD8AAAAAN7QaQqFZuh5r2g+FR/AJAAhdxdp2221nhwqogaiGBzh91ZYvX07jfwAAAASK+nJpsmQqWhFB/67iBp+pEHwCAAIVrF166aXm/PPPN5WVlba/mjMkQNVrEyZMyMcxAgAAAHmjqV8aihUf4CiwYeJkcRF8AgD8rs9GjdpxSZN3NJ1nt912s8tA5cUXX7QVa5rYGeYxqgAAAAgnvSx2muUrZFMVFIGNP+h1us4/YpfsKvhUqEbwCQAoZk6UVbAm//znP817771nBwZsuumm9oVIUF94EKwBAAAA/kbwCQDwY07keinoqlWrzLRp00xVVZU5/PDD7TtHcsopp5jzzjsv+6MGAAAAgCQUopWUlJjS0lJ7SagGAPAD18HaOeecY8uulyxZYjbbbLPu7ccff7x5+OGHvT4+AAAAAAAAwJdcz6XWkAJNA91qq616bN9+++3NBx984OWxAQAAAAAAAOEJ1tTXILZSzbF69WqzySabeHVcAAAAQCjRKwwAgAgvBZ0yZYq56667uq/rRcCGDRvMtddea6ZOner18QEAAAChoSbIzc3NprW11Xz44Yf2Ute1HQAARKBiTQGahhe89NJL5quvvjIXXniheeutt2zF2rPPPpufowQAAAACTuHZ0qVLe21X5ZqzXRPIAABAiCvWdt55Z/POO++Y/fbbz9TU1Ngy9unTp5tXXnnFbLvttvk5SgAAAJ9Yv369aWpqMgsWLLCXug5ksvzz448/TrlPW1ub3Q8AAIS0Yq2rq8sceuih5pZbbjEXX3xx/o4KAADAhxobG82sWbPsEj6HBjrV19fbNxqBZJyeaulea2u/kpKSgh0XAAAoYMXagAEDzOuvv57jtwQAAAhmqDZjxoweoZosW7bMbtftyK8gVwumC9Xc7gf4kSouOzs7TXt7u72kAhNAFLheCvof//Ef5vbbb8/P0QAAAPiQAhxVqiU6SXS21dbWBiroCRoFl5WVlXZY1syZM+2lrvsx0EwULmj6ZyYy3Q/wGwZzAIgq13+59S7aHXfcYR5//HGzxx572PHgsebNm+fl8QEAABTd008/3atSLZaCEzWf137V1dUFPbYoVQvGB5tOtWBDQ4NvluIqRFAvtdjKM4VlW265pb1MVZGm1SHxr62BIGAwB4Aocx2svfnmm2bixIn2cw0xiNWnTx/vjgwAAMAn0jWdd7sfvKsW1OtPVQtqqFa/fv2Mn8OFESNGmFWrViX992VlZbyeRmgHcwwdOpTHN4BQch2sLVq0KD9HAgAA4FOqNvJyP4SvWjCTcKGjo8MOu1DIEFu5pko1hWpU9MB5LDnDLlTlqCpGPwdSDOYAEHU0cQAAAEhjypQpNhDR0sNElVM66dXt2g/RrBbMNFxQUDJu3LhABSdRDYz8tpTYr8ErgzkARB3BGgAA8O0SQFUh6SRTJ5UKrYq11E/ft76+3vbzUhAQG645wUBdXV3RlyKGUVCqBd2EC3rMRK1yJ4iBUaEFtU8ZgzkARJ3rqaAAAABRnACp5vhqkl9eXt5juyrV/NQ8P6zVgskqm7S9oqKi6NWChAvpA6P48NEJjJgamXmfskQVs8WmysN0j2sGcwAIM4I1AADgywmQ8X21nAmQxQ7XWltbbc/Z+fPnm2eeeca8+uqr5uCDD/blCW8YONWCEh+u+alakHAhfIGRX/uU+Y1+D9NVjDKYA0CYEawBAIDATIAUTYDUfsWiAGfChAn2Q8uyPvroIxu2NTc3U3kT4WpBwoXwBUaFFPQ+ZXouVOVofLisMFnb/biEFQC84roW/YEHHki4XS8SBg0aZLbbbjszduxYL44NAABETBAmQAa1D1LQKTyrqanxTd+9RJyfe3wvsShP/Qx6YFQoYVhKrMf30KFDGVABIHJcPzMfc8wxvZr2irNNl/vtt5/5v//7PzNs2DAvjxUAAISc3ydAZrqsTSeXnEx6TyFasQLVTBEuhC8wKuRS4lQBYxCWEkdxMAcAuF4K+thjj5lvfvOb9lLv2OpDn++1117moYceMk899ZRZtWqVOf/88/NzxAAAILT8PgGSZW1wEy6Ulpbay6iGakLvucywlBgAgsv1W0Pqe3LrrbeayZMnd2+bNm2aXQZ62mmnmbfeess2kD355JO9PlYAABCRCZAaVJCoz5pOKnV7sSZAsqwNyC4wSrR82kFg9C8sJQaAiARr7733ni1vj6dt77//vv18++23NytXrvTmCAEAQOQmQGr6Z3zrCT9MgGRZG+AegVE4lxLr+TkIxwkA+eb6Vd8ee+xhLrjgAnPXXXeZkSNH2m0rVqwwF154oV0iKu+++66d/gIAAJDtBEhVyccOMlClmkK1Yk6ADEsfJKDQghQYFVsQ+pSpHVB8UKqfqaoTY4NSwjcAUdBnY6J1FilolLwmMrW0tHSHZyrt3mabbcz9999vqqqq7OCCzz//3Jx44okmCDo6OuwfAP2BSFSNBwAACm/9+vW+nACZbCqoQ6+PqMABEFaZPgdmGr4BQNBzItfBmmzYsME8+uij5p133rHXx40bZ771rW+Zvn1dz0LwBYI1AADgRqITRpa1AQg7nTqq0CJd1a6eC3kDAkCQ5T1YCxuCNQAA4BZLnABETWdnp2ltbU27n6qLVXWcKnzTSieeMwGEISfKqrPuwoUL7cfy5ctt9VqsO+64I5svCQAAEChB6IPkF35d1gvAnUwnHqcK1aSrq8u+McFzKIAwcB2szZkzx1x++eVm0qRJ9oUR7zIAAAAgmcbGxoSDKDT9tZiDKAC45+XE40xDOgDwO9fPjLfccou58847AzOYAAAAAMUL1WbMmGGXzcZatmyZ3a7pr4RrwcIS6GjLZDJyumWg+QjpAKCYXE8b+Oqrr8zkyZPzczQAAAAIBZ1Yq1ItUTtfZ1ttbW1GJ+DwB/WZUeN69dhSBaIudV3bEQ0KUbVqKRXdni40U481hXQAEMlg7dRTTzXz58/Pz9EAAAAgFNRT7aOPPrLtQw477DB7GTtBXuGapgZqP/ifwjP9vOIrlXRd2wnXokPNvDXVMz48U1im7aWlpWnDN00NpdIRQFi4rr9dt26dufXWW83jjz9udt11V/sEGmvevHleHh8AAAACSMsFH3nkEXsC7WhrazNz5861Q7AcGmgAf1MImu7npJ+tpqYRlkQnXNPPO9myYN0uetzEhrE6d9RzgnM7AEQyWHv99dfN7rvvbj9/8803e9zGH1IAAACoemnrrbfutQx01KhR9k3Yc889tztcS1fZguJzwpNUmPIYPekmI6cL3wAgssHaokWL8nMkAAAALqk/l5YSqipCAc2UKVNs42wUv7pJJ8/xJ9BaCrphwwZz0UUXmaamJjN69Gj7M4O/ZTq9kSmPcBu+AUAY5DSKxRmbrpHpAAAAhZ44qeb4zusR5zVJfX09kyZ9XN2kcE0h6MSJE83s2bMJQgMg0+mNTHlEWDD9FkBehxfoXcbLL7/clvaOGTPGfqhB5RVXXGFvAwAAKESoNmPGjB6hmixbtsxu1+0ojkyrlq666ioC0IBQqMCUR0QF028BuOX6baWLL77Y3H777bbx7L777mu3PfPMM+bnP/+5HWygF0kAAAD5XP6pSrX4/l2ibaoqqK2tNTU1Na6qoVhW6o1Mq5b22WefvB9LVHldbaN/q98JTf9MhimPCNP023jO9Fth8AKAnIO1//3f/zW/+c1vzNFHH929TdNBy8vLzU9+8hOCNQAAkFcKv+Ir1eJDBZ0Aab/q6uqMvibLSr2vbkpXuUY/rvwFA/GTGPXzUDCWSyDAlMfoiOoySKbfAihYsLZ69Wozfvz4Xtu1TbcBAADkU7oTH7f7OctK4yvgnGWlDQ0NhGseVzeJQkztSyATnGobpjyGX76C2SBg+i2AgvVY22233cwNN9zQa7u26TYAAIB80gmeV/ulW1YqWlaq/ZA5nYBXVFSk3U/VH4nue+Sv2ibX+9uZ8qgey7okVAtfMBsfLjnBbNh7jDH9FkDBKtauvfZac8QRR5jHH3+8uzfGc889Z59s//znP2d9IAAAAJlQ7zMt01RFWaKQQCf6ul37FWNZKf4lk/50VH94h2ob5IJlkEy/BVDAirUDDjjAvPPOO+bYY4817e3t9kPLIzQpJZMXsAAARJkqn5qamsyCBQvsJZVQ2QU26n0m8Sd4zvW6urqMgh2vl5Xi36j+KCzubxQqmA0rpt8CyFZWcfvo0aMZUgAAgEs0yPeO7i/1Pkt0fypUy/T+9HJZKXqi+qOwuL+RC4JZpt8CyF6fjVk0Wli3bp15/fXXzfLly82GDRt63BY7LTQoOjo6bC8Q9Q1QeTMAAF5L1iDfeYFOg/zsqOJPyzRVUaYTIlXPZ1KpFvvvKysr0y4rbWlpcfV18a+lZVrRkOpEXNUfVVVVnKh6gPsbuejs7DStra1p99PzZdiXEica4MD0WyB6OlzkRK6DtYcffth8//vfNytXruz9xfr0CeSSFoI1AEA+OeFNsl5ehDf+CD0l9mWRH0LPXINDv06pdGjAASeq3uH+RrYIZnvfH0y/BaKtw0VO5LrH2llnnWW+/e1v2xd4qlaL/QhiqAYAQL65aZCP4i0rLS8v77FdYWcxQzUFfgpkp06dambOnGkvdV3bgzYdNH75oU7QCXm8x/2NXJdBphKlZZBMvwXghusmC5988ok599xzzRZbbOH2nwIAEEk0yPc/hWc1NTW+qQ5LtnRYS1a1PUhLhxXm6J1eqj8Kg/sb2XKCV5ZBAkCegzW9mNMUs2233dbtPwUAIJL82CA/6EsM80H//+rq6mIfhv3ZaChDom4d2qaApLa21gaBQfmZOdUfKAzub2SLYBYA3HPdY23t2rV2KejIkSPNLrvsYt/BiHX22WeboKHHGgAgn/zWIJ/ppP6mNzC17DOdRYsW+SIIBAAACBs3OZHrirUFCxaYRx991AwaNMi+8It990KfBzFYAwAgnxSWKbRS1bf+VsY3yNf1U0891dx77715rx4L0xLDsGLpMAAAQIgr1rS+XuHZ7NmzTd++rmcf+BIVawCAQkhUKTZixAh7uWrVqrxXjzGdNBioWAMAAAhOTuQ6WBs+fLj529/+FqoeawRrAIBi9DZ79913zc9//vNe1WNONbjX1WMENsHgt6XDAAAAUdPhIidyXXJ20kknmXvuuSeX4wMAwES9Qf53vvMdc9tttyVtUC9qUK+QxSssMQzW0mGJbxjuXK+rqyNUAwAA8AHXPdb0Av/aa681jzzyiNl11117DS+YN2+el8cHAEAoqWot2ZJMJ1xbunSp3c+r6jE/TidFYqpUVMVioiETCtXogwcAABDQYO2NN94wEyZMsJ+/+eabPW5jDDMAAP6tHtNQBAUz6ZYYaj8Un8Kzmpqa7qXD+R5sAQAAgAIEa+q7AgAAglc9lm46qbDE0J9LhwEAAOBP4RjrCQBAwDjVY8mqvbW9oqLC8+oxZ4lheXl5j+06Fq+HJQAAAABh53oqqLz00kvm3nvvNUuWLDFfffVVj9saGxtN0DAVFABQDPqbqeoxSVQ9ls+gK3Y6KUsMAQAAgAJNBb377rvN5MmTzdtvv23uu+8+09XVZd566y3zxBNP2G8KAPAu+GhqajILFiywl15Oh4Q/FLN6zFlieMIJJ9hLQjUAAACgABVrmgT6n//5n+aMM84wQ4YMMa+99poZO3as3aZ3vOfMmWOChoo1AH6sZEo0DVD9sViqFz6FqB6jQg0AAADwPidyHawNHjzYVqhVVlaaESNG2CqKXXbZxVawHXjggZ5OLysUgjUAflweGP/0XIjlgQgngloAAADAJ0tBhw0bZj7//HP7uZauvPnmm/bz9vZ2s3btWrdfDgAQV1WkACTRex7OttraWpaFwnVQGxuqybJly+z2IPZGBQAAAPzCdbC2//77m8cee8x+/u1vf9ueAP7oRz+yPVqmTZuWj2MEgMjQUr34ACQ+XFu6dKndD0iHoBYAAADIr/5u/8ENN9xg1q1bZz+/+OKLzYABA8xf//pXc9xxx5mf/exn+ThGAIiMTJfTB3HZPZJTyLVmzRrz9ddfm/79+9u2C87S30IFtRpgAAAAACDPwdrw4cO7P+/bt6+ZPXu22y8BAEhCTeW93A/+p74NCkoVqjkUrulnnOu0bYJaAAAAwGdLQQEA+aNJjWoqn6xaSdsrKirsfghHqKaKsdhQTXRd23V7Lghq3VEFX2dnp+0bq0uX850AAAAQQQRrAOAj/fr1s5MaJT5cc67X1dXZ/RBsCm3SVYq1tbXlFO4Q1GZOIWZzc7NpbW21y2d1qeuJwk0COAAAADgI1gDAZ6ZPn24aGhrs5OVYCki0XbejJzXfb2pqMgsWLLCXQWjG7/RUS6Wrq8vuly2CWu8rB90EcAAAAAg/gjUA8CGFZzphX7RokZk/f769bGlpIVRLoLGx0VRWVpqpU6eamTNn2ktd13Y/Sxequd0vGYJa7yoH8710FwAAAMHTZ6PL9Qsnn3yyffd7yJAhPbbrHfWzzjrL3HHHHSZoOjo6bINovSAeOnRosQ8HAJAhhWczZszotRTPqcbyc3CkJYQKT9NRSFhSUpLz91MVn6Z/KkRSTzUt/4x6pZrbn4Mq1FIFnZqUXlVV5clEVwAAAAQjJ3IdrOlFuF6Ujxo1qsf2lStXmrKyspzfWS8GgjUACB4FRU7YkYjCDVVlqdLPjwGS/vxqCSFBTXGpT1qyx1Csb3zjG/a1TqGCUAAIKv19c9odaMr14MGD+TsGINQ5UX83X1RPkvr4/PPPzaBBg3qc3Pz5z3/uFbYBAIIrmwqnQlZF6fukCkT090rL87RfdXW18RudZOg+0jEmozesOBnJL530eSmIbzACgFd0AqrXALHPhXqe1d87naACQBhl/GqytLTUvrjXh949j6ftc+bM8fr4AABFWmI5a9asHsGVqr/UCiDZ0sps/k0u0vXFcrtfMTgnGfEnIapUU6jGSUj+qZJCJ33pKge1XyYVa14HdQAQFE4fynhOH0rh7xqAMMr41Z8aZ+vd/wMPPND88Y9/NMOHD+++beDAgWbMmDFm9OjR+TpOAECR+5YtW7bMbk/Utyybf5Mrvfvt5X7FopMMlZezbMbflYNa3plpAAcAUZPpIBj9vePvG4CwcdVjTS8mf/SjH5nLL7/cVFRUmLCgxxoAZN+3rFi9zpzvq/Au0Z8yv/dYg/+XL8VXDiarxnDotRHVGACiqNADeQDATzlRXzdfWO/UqupAJzMAgPBx07csl3/jBYVlWmYq8e9+O9fr6uoI1ZARvXAaN26cPelTIKtLtb6IDcr0ucKz+OWeCuAI1QBEWab9JelDCSCMXAVroqWgTz75ZH6OBgBQVNn0LStmrzMtL9UbPuXl5T22KxjJx/JThJsCWVVSqK+sLhMtV8okgAOAqMm0vyR9KAGEketntsMOO8zMnj3bvPHGG2aPPfbo1Uvk6KOP9vL4AAAFlE3fsmL3OlN4VlNTU7BppIATwAEA3A+CAYBI91iTvn37pnyhGcRlovRYA4Ds+5bR6wwIN/1eM1wDQDr0oQQQJnnrsSYbNmxI+hHEUA0AkFvfMnqdAeGlF5PNzc22Kbl6KepS17UdAGLRhxJAVLkO1gAA4ZZN3zJ6nQHhrT6JX9ql69pOuAYgHn0oAUSR66WgouEFv/zlL83bb79tr++4447mggsusD1tgoiloADQm6qQ3fYty+bfAPAfvTxUZVq6fkk6YWZZKAAACBs3OZHrYO13v/ud+eEPf2irD/bdd1+77dlnnzX33XefufPOO83MmTN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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medians by group\n",
      "Locally born parents\n",
      "74.48896156991006\n",
      "Immigrant parents\n",
      "69.82769388274633\n",
      "Respondent born abroad\n",
      "61.50452449036953\n",
      "Average differences between groups\n",
      "Locally born parents cf immigrant parents\n",
      "6.7984237650100585\n",
      "Immigrant parents cf respondent born abroad\n",
      "6.794705142103214\n",
      "Locally born parents cf respondent born abroad\n",
      "13.424963076726893\n"
     ]
    }
   ],
   "source": [
    "#Figure 4.2\n",
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    immigstatus=makescattertable('immigstatus')\n",
    "immigstatus=immigstatus.loc[((immigstatus['group_count_Born abroad']>0)&(immigstatus['group_count_Immigrant parent(s)']>0)&(immigstatus['group_count_Born abroad']>0))].copy()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "plt.scatter(immigstatus['pc_Agree_Local parent(s)'],immigstatus['pc_Agree_Immigrant parent(s)'],color='k')\n",
    "plt.scatter(immigstatus['pc_Agree_Local parent(s)'],immigstatus['pc_Agree_Born abroad'],color='lightgray')\n",
    "plt.xlabel('Support among respondents with locally born parents')\n",
    "plt.ylabel('Support among respondents with immigrant parent(s)/born abroad')\n",
    "plt.show()\n",
    "\n",
    "print('Medians by group')\n",
    "print('Locally born parents')\n",
    "print(immigstatus['pc_Agree_Local parent(s)'].median())\n",
    "print('Immigrant parents')\n",
    "print(immigstatus['pc_Agree_Immigrant parent(s)'].median())\n",
    "print('Respondent born abroad')\n",
    "print(immigstatus['pc_Agree_Born abroad'].median())\n",
    "print('Average differences between groups')\n",
    "print('Locally born parents cf immigrant parents')\n",
    "print((immigstatus['pc_Agree_Local parent(s)']-immigstatus['pc_Agree_Immigrant parent(s)']).mean())\n",
    "print('Immigrant parents cf respondent born abroad')\n",
    "print((immigstatus['pc_Agree_Immigrant parent(s)']-immigstatus['pc_Agree_Born abroad']).mean())\n",
    "print('Locally born parents cf respondent born abroad')\n",
    "print((immigstatus['pc_Agree_Local parent(s)']-immigstatus['pc_Agree_Born abroad']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "83b19e92-2691-4643-ad06-60fcca5b18d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medians by group\n",
      "Born locally, Ethnic plurality\n",
      "75.71783002470823\n",
      "Born locally, Ethnic minority\n",
      "71.58870729030124\n",
      "Born abroad, Ethnic plurality\n",
      "58.8235294117647\n",
      "Born abroad, Ethnic minority\n",
      "65.56944880175226\n",
      "Average differences between groups\n",
      "Born locally, Ethnic plurality - Born locally, ethnic minority\n",
      "2.420783457220328\n",
      "Born locally, Ethnic plurality - Born abroad, ethnic plurality\n",
      "8.230997192485804\n",
      "Born locally, Ethnic plurality - Born abroad, ethnic minority\n",
      "9.87327856314312\n",
      "Born locally, Ethnic minority - Born abroad, Ethnic plurality\n",
      "5.048995177094024\n",
      "Born locally, Ethnic minority - Born abroad, Ethnic minority\n",
      "6.702230546750212\n",
      "Born abroad, Ethnic plurality - Born abroad, Ethnic minority\n",
      "1.1186083428334226\n"
     ]
    }
   ],
   "source": [
    "#Figure 4.3\n",
    "with warnings.catch_warnings(action=\"ignore\"): #Suppress warnings about Country samples with missing categories\n",
    "    bplxeth=makescattertable('bplXeth')\n",
    "bplxeth=bplxeth.loc[((bplxeth['group_count_Born locally, Ethnic minority']>0)&(bplxeth['group_count_Born abroad, Ethnic plurality']>0)&(bplxeth['group_count_Born abroad, Ethnic minority']>0))].copy()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "plt.scatter(bplxeth['pc_Agree_Born locally, Ethnic plurality'],bplxeth['pc_Agree_Born locally, Ethnic minority'],color='k')\n",
    "plt.scatter(bplxeth['pc_Agree_Born locally, Ethnic plurality'],bplxeth['pc_Agree_Born abroad, Ethnic minority'],color='lightgray')\n",
    "plt.scatter(bplxeth['pc_Agree_Born locally, Ethnic plurality'],bplxeth['pc_Agree_Born abroad, Ethnic plurality'],color='k',marker='s')\n",
    "\n",
    "plt.xlabel('Support among respondents from ethnic plurality and born locally')\n",
    "plt.ylabel('Support among respondents from ethnic minority and/or born abroad')\n",
    "plt.show()\n",
    "\n",
    "print('Medians by group')\n",
    "print('Born locally, Ethnic plurality')\n",
    "print(bplxeth['pc_Agree_Born locally, Ethnic plurality'].median())\n",
    "print('Born locally, Ethnic minority')\n",
    "print(bplxeth['pc_Agree_Born locally, Ethnic minority'].median())\n",
    "print('Born abroad, Ethnic plurality')\n",
    "print(bplxeth['pc_Agree_Born abroad, Ethnic plurality'].median())\n",
    "print('Born abroad, Ethnic minority')\n",
    "print(bplxeth['pc_Agree_Born abroad, Ethnic minority'].median())\n",
    "print('Average differences between groups')\n",
    "print('Born locally, Ethnic plurality - Born locally, ethnic minority')\n",
    "print((bplxeth['pc_Agree_Born locally, Ethnic plurality']-bplxeth['pc_Agree_Born locally, Ethnic minority']).mean())\n",
    "print('Born locally, Ethnic plurality - Born abroad, ethnic plurality')\n",
    "print((bplxeth['pc_Agree_Born locally, Ethnic plurality']-bplxeth['pc_Agree_Born abroad, Ethnic plurality']).mean())\n",
    "print('Born locally, Ethnic plurality - Born abroad, ethnic minority')\n",
    "print((bplxeth['pc_Agree_Born locally, Ethnic plurality']-bplxeth['pc_Agree_Born abroad, Ethnic minority']).mean())\n",
    "print('Born locally, Ethnic minority - Born abroad, Ethnic plurality')\n",
    "print((bplxeth['pc_Agree_Born locally, Ethnic minority']-bplxeth['pc_Agree_Born abroad, Ethnic plurality']).mean())\n",
    "print('Born locally, Ethnic minority - Born abroad, Ethnic minority')\n",
    "print((bplxeth['pc_Agree_Born locally, Ethnic minority']-bplxeth['pc_Agree_Born abroad, Ethnic minority']).mean())\n",
    "print('Born abroad, Ethnic plurality - Born abroad, Ethnic minority')\n",
    "print((bplxeth['pc_Agree_Born abroad, Ethnic plurality']-bplxeth['pc_Agree_Born abroad, Ethnic minority']).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c33cb5-9444-4e92-99a7-a87b581dd812",
   "metadata": {},
   "source": [
    "#### Table A.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "96a84266-9a37-4618-acba-c88d40a2382a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ivs['jobsagree']=(ivs['jobs']=='Agree')*1\n",
    "ui1=smf.ols(formula=\"jobsagree ~ C(ntrlzd) + C(Country) + C(Year)\",\n",
    "            data=ivs).fit()\n",
    "ui2=smf.ols(formula=\"jobsagree ~ C(immigstatus, Treatment(reference='Local parent(s)')) + C(Country) + C(Year)\", \n",
    "             data=ivs).fit()\n",
    "ui3=smf.ols(formula=\"jobsagree ~ C(bplXeth, Treatment(reference='Born locally, Ethnic plurality')) + C(Country) + C(Year)\",\n",
    "            data=ivs).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f9b01982-372f-4b17-98fa-27d0cc452509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"text-align:center\"><tr><td colspan=\"4\" style=\"border-bottom: 1px solid black\"></td></tr>\n",
       "<tr><td style=\"text-align:left\"></td><td colspan=\"3\"><em>Dependent variable: jobsagree</em></td></tr><tr><td style=\"text-align:left\"></td><tr><td style=\"text-align:left\"></td><td>(1)</td><td>(2)</td><td>(3)</td></tr>\n",
       "<tr><td colspan=\"4\" style=\"border-bottom: 1px solid black\"></td></tr>\n",
       "\n",
       "<tr><td style=\"text-align:left\">C(ntrlzd)[T.Naturalized]</td><td>-0.128<sup>***</sup></td><td></td><td></td></tr>\n",
       "<tr><td style=\"text-align:left\"></td><td>(0.006)</td><td></td><td></td></tr>\n",
       "<tr><td style=\"text-align:left\">C(ntrlzd)[T.Non-national]</td><td>-0.265<sup>***</sup></td><td></td><td></td></tr>\n",
       "<tr><td style=\"text-align:left\"></td><td>(0.006)</td><td></td><td></td></tr>\n",
       "<tr><td style=\"text-align:left\">C(immigstatus, Treatment(reference='Local parent(s)'))[T.Immigrant parent(s)]</td><td></td><td>-0.070<sup>***</sup></td><td></td></tr>\n",
       "<tr><td style=\"text-align:left\"></td><td></td><td>(0.004)</td><td></td></tr>\n",
       "<tr><td style=\"text-align:left\">C(immigstatus, Treatment(reference='Local parent(s)'))[T.Born abroad]</td><td></td><td>-0.179<sup>***</sup></td><td></td></tr>\n",
       "<tr><td style=\"text-align:left\"></td><td></td><td>(0.004)</td><td></td></tr>\n",
       "<tr><td style=\"text-align:left\">C(bplXeth, Treatment(reference='Born locally, Ethnic plurality'))[T.Born locally, Ethnic minority]</td><td></td><td></td><td>-0.008<sup>**</sup></td></tr>\n",
       "<tr><td style=\"text-align:left\"></td><td></td><td></td><td>(0.004)</td></tr>\n",
       "<tr><td style=\"text-align:left\">C(bplXeth, Treatment(reference='Born locally, Ethnic plurality'))[T.Born abroad, Ethnic plurality]</td><td></td><td></td><td>-0.155<sup>***</sup></td></tr>\n",
       "<tr><td style=\"text-align:left\"></td><td></td><td></td><td>(0.009)</td></tr>\n",
       "<tr><td style=\"text-align:left\">C(bplXeth, Treatment(reference='Born locally, Ethnic plurality'))[T.Born abroad, Ethnic minority]</td><td></td><td></td><td>-0.180<sup>***</sup></td></tr>\n",
       "<tr><td style=\"text-align:left\"></td><td></td><td></td><td>(0.006)</td></tr>\n",
       "\n",
       "<td colspan=\"4\" style=\"border-bottom: 1px solid black\"></td></tr>\n",
       "<tr><td style=\"text-align: left\">Observations</td><td>170124</td><td>211312</td><td>211312</td></tr><tr><td style=\"text-align: left\">R<sup>2</sup></td><td>0.165</td><td>0.173</td><td>0.172</td></tr><tr><td style=\"text-align: left\">Adjusted R<sup>2</sup></td><td>0.165</td><td>0.173</td><td>0.172</td></tr><tr><td style=\"text-align: left\">Residual Std. Error</td><td>0.435 (df=170066)</td><td>0.430 (df=211198)</td><td>0.431 (df=211197)</td></tr><tr><td style=\"text-align: left\">F Statistic</td><td>591.047<sup>***</sup> (df=57; 170066)</td><td>392.153<sup>***</sup> (df=113; 211198)</td><td>385.619<sup>***</sup> (df=114; 211197)</td></tr>\n",
       "<tr><td colspan=\"4\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Note:</td><td colspan=\"3\" style=\"text-align: right\"><sup>*</sup>p&lt;0.1; <sup>**</sup>p&lt;0.05; <sup>***</sup>p&lt;0.01</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Stargazer object\n",
    "stargazer = Stargazer([ui1,ui2,ui3])\n",
    "\n",
    "# Select and order variables\n",
    "stargazer.covariate_order(['C(ntrlzd)[T.Naturalized]','C(ntrlzd)[T.Non-national]',\n",
    "                           'C(immigstatus, Treatment(reference=\\'Local parent(s)\\'))[T.Immigrant parent(s)]',\n",
    "                           'C(immigstatus, Treatment(reference=\\'Local parent(s)\\'))[T.Born abroad]'\n",
    "   ,'C(bplXeth, Treatment(reference=\\'Born locally, Ethnic plurality\\'))[T.Born locally, Ethnic minority]'\n",
    "   ,'C(bplXeth, Treatment(reference=\\'Born locally, Ethnic plurality\\'))[T.Born abroad, Ethnic plurality]'\n",
    "   ,'C(bplXeth, Treatment(reference=\\'Born locally, Ethnic plurality\\'))[T.Born abroad, Ethnic minority]'\n",
    "])\n",
    "\n",
    "HTML(stargazer.render_html())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a6cbb08-7b90-419a-a49e-0c33887cec91",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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